├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── data_provider ├── __init__.py ├── data_factory.py ├── data_loader.py ├── m4.py └── uea.py ├── exp ├── __init__.py ├── exp_anomaly_detection.py ├── exp_basic.py ├── exp_classification.py ├── exp_imputation.py ├── exp_long_term_forecasting.py └── exp_short_term_forecasting.py ├── layers ├── AutoCorrelation.py ├── Autoformer_EncDec.py ├── Conv_Blocks.py ├── Crossformer_EncDec.py ├── DWT_Decomposition.py ├── ETSformer_EncDec.py ├── Embed.py ├── FourierCorrelation.py ├── MultiWaveletCorrelation.py ├── Pyraformer_EncDec.py ├── SelfAttention_Family.py ├── StandardNorm.py ├── Transformer_EncDec.py └── __init__.py ├── models ├── Autoformer.py ├── Crossformer.py ├── DLinear.py ├── ETSformer.py ├── FEDformer.py ├── FiLM.py ├── FreTS.py ├── Informer.py ├── Koopa.py ├── LightTS.py ├── MICN.py ├── Mamba.py ├── MambaSimple.py ├── MultiPatchFormer.py ├── Nonstationary_Transformer.py ├── PAttn.py ├── PatchTST.py ├── Pyraformer.py ├── Reformer.py ├── SCINet.py ├── SegRNN.py ├── TSMixer.py ├── TemporalFusionTransformer.py ├── TiDE.py ├── TimeMixer.py ├── TimeXer.py ├── TimesNet.py ├── Transformer.py ├── WPMixer.py ├── __init__.py └── iTransformer.py ├── pic └── dataset.png ├── requirements.txt ├── run.py ├── scripts ├── anomaly_detection │ ├── MSL │ │ ├── Autoformer.sh │ │ ├── Crossformer.sh │ │ ├── DLinear.sh │ │ ├── ETSformer.sh │ │ ├── FEDformer.sh │ │ ├── FiLM.sh │ │ ├── Informer.sh │ │ ├── LightTS.sh │ │ ├── MICN.sh │ │ ├── Pyraformer.sh │ │ ├── Reformer.sh │ │ ├── TimesNet.sh │ │ ├── Transformer.sh │ │ └── iTransformer.sh │ ├── PSM │ │ ├── Autoformer.sh │ │ ├── DLinear.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh │ ├── SMAP │ │ ├── Autoformer.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh │ ├── SMD │ │ ├── Autoformer.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh │ └── SWAT │ │ ├── Autoformer.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh ├── classification │ ├── Autoformer.sh │ ├── Crossformer.sh │ ├── DLinear.sh │ ├── ETSformer.sh │ ├── FEDformer.sh │ ├── FiLM.sh │ ├── Informer.sh │ ├── LightTS.sh │ ├── MICN.sh │ ├── PatchTST.sh │ ├── Pyraformer.sh │ ├── Reformer.sh │ ├── TimesNet.sh │ ├── Transformer.sh │ └── iTransformer.sh ├── exogenous_forecast │ ├── ECL │ │ └── TimeXer.sh │ ├── EPF │ │ └── TimeXer.sh │ ├── ETTh1 │ │ └── TimeXer.sh │ ├── ETTh2 │ │ └── TimeXer.sh │ ├── ETTm1 │ │ └── TimeXer.sh │ ├── ETTm2 │ │ └── TimeXer.sh │ ├── Traffic │ │ └── TimeXer.sh │ └── Weather │ │ └── TimeXer.sh ├── imputation │ ├── ECL_script │ │ ├── Autoformer.sh │ │ ├── DLinear.sh │ │ ├── ETSformer.sh │ │ ├── FEDformer.sh │ │ ├── Informer.sh │ │ ├── LightTS.sh │ │ ├── Pyraformer.sh │ │ ├── Reformer.sh │ │ ├── TimesNet.sh │ │ ├── Transformer.sh │ │ └── iTransformer.sh │ ├── ETT_script │ │ ├── Autoformer_ETTh1.sh │ │ ├── Autoformer_ETTh2.sh │ │ ├── Autoformer_ETTm1.sh │ │ ├── Autoformer_ETTm2.sh │ │ ├── Crossformer_ETTh1.sh │ │ ├── DLinear_ETTh1.sh │ │ ├── FiLM_ETTh1.sh │ │ ├── MICN_ETTh1.sh │ │ ├── Nonstationary_Transformer_ETTh1.sh │ │ ├── TiDE_ETTh1.sh │ │ ├── TimesNet_ETTh1.sh │ │ ├── TimesNet_ETTh2.sh │ │ ├── TimesNet_ETTm1.sh │ │ ├── TimesNet_ETTm2.sh │ │ ├── Transformer_ETTh1.sh │ │ ├── Transformer_ETTh2.sh │ │ ├── Transformer_ETTm1.sh │ │ ├── Transformer_ETTm2.sh │ │ └── iTransformer_ETTh2.sh │ └── Weather_script │ │ ├── Autoformer.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh ├── long_term_forecast │ ├── AugmentSample │ │ ├── Classification │ │ │ └── PatchTST.sh │ │ ├── Forecasting │ │ │ └── PatchTST.sh │ │ └── ReadMe.md │ ├── ECL_script │ │ ├── Autoformer.sh │ │ ├── Crossformer.sh │ │ ├── DLinear.sh │ │ ├── ETSformer.sh │ │ ├── FEDformer.sh │ │ ├── FiLM.sh │ │ ├── Informer.sh │ │ ├── Koopa.sh │ │ ├── LightTS.sh │ │ ├── MICN.sh │ │ ├── Mamba.sh │ │ ├── MultiPatchFormer.sh │ │ ├── Nonstationary_Transformer.sh │ │ ├── PatchTST.sh │ │ ├── Pyraformer.sh │ │ ├── Reformer.sh │ │ ├── SegRNN.sh │ │ ├── TSMixer.sh │ │ ├── TimeMixer.sh │ │ ├── TimeXer.sh │ │ ├── TimesNet.sh │ │ ├── Transformer.sh │ │ ├── WPMixer.sh │ │ └── iTransformer.sh │ ├── ETT_script │ │ ├── Autoformer_ETTh1.sh │ │ ├── Autoformer_ETTh2.sh │ │ ├── Autoformer_ETTm1.sh │ │ ├── Autoformer_ETTm2.sh │ │ ├── Crossformer_ETTh1.sh │ │ ├── Crossformer_ETTh2.sh │ │ ├── Crossformer_ETTm1.sh │ │ ├── Crossformer_ETTm2.sh │ │ ├── DLinear_ETTh1.sh │ │ ├── ETSformer_ETTh1.sh │ │ ├── FEDformer_ETTh1.sh │ │ ├── FiLM_ETTh1.sh │ │ ├── FiLM_ETTh2.sh │ │ ├── FiLM_ETTm1.sh │ │ ├── FiLM_ETTm2.sh │ │ ├── Informer_ETTh1.sh │ │ ├── Koopa_ETTh1.sh │ │ ├── Koopa_ETTh2.sh │ │ ├── Koopa_ETTm1.sh │ │ ├── Koopa_ETTm2.sh │ │ ├── LightTS_ETTh1.sh │ │ ├── MICN_ETTh1.sh │ │ ├── MICN_ETTh2.sh │ │ ├── MICN_ETTm1.sh │ │ ├── MICN_ETTm2.sh │ │ ├── MambaSimple_ETTh1.sh │ │ ├── Mamba_ETT_all.sh │ │ ├── Mamba_ETTh1.sh │ │ ├── Mamba_ETTh2.sh │ │ ├── Mamba_ETTm1.sh │ │ ├── Mamba_ETTm2.sh │ │ ├── MultiPatchFormer_ETTh1.sh │ │ ├── MultiPatchFormer_ETTm1.sh │ │ ├── Nonstationary_Transformer_ETTh1.sh │ │ ├── Nonstationary_Transformer_ETTh2.sh │ │ ├── Nonstationary_Transformer_ETTm1.sh │ │ ├── Nonstationary_Transformer_ETTm2.sh │ │ ├── PAttn_ETTh1.sh │ │ ├── PatchTST_ETTh1.sh │ │ ├── PatchTST_ETTh2.sh │ │ ├── PatchTST_ETTm1.sh │ │ ├── PatchTST_ETTm2.sh │ │ ├── Pyraformer_ETTh1.sh │ │ ├── Pyraformer_ETTh2.sh │ │ ├── Pyraformer_ETTm1.sh │ │ ├── Pyraformer_ETTm2.sh │ │ ├── Reformer_ETTh1.sh │ │ ├── SegRNN_ETTh1.sh │ │ ├── SegRNN_ETTh2.sh │ │ ├── SegRNN_ETTm1.sh │ │ ├── SegRNN_ETTm2.sh │ │ ├── TSMixer_ETTh1.sh │ │ ├── TSMixer_ETTh2.sh │ │ ├── TSMixer_ETTm1.sh │ │ ├── TSMixer_ETTm2.sh │ │ ├── TiDE_ETTh1.sh │ │ ├── TimeMixer_ETTh1.sh │ │ ├── TimeMixer_ETTh2.sh │ │ ├── TimeMixer_ETTm1.sh │ │ ├── TimeMixer_ETTm2.sh │ │ ├── TimeXer_ETTh1.sh │ │ ├── TimeXer_ETTh2.sh │ │ ├── TimeXer_ETTm1.sh │ │ ├── TimeXer_ETTm2.sh │ │ ├── TimesNet_ETTh1.sh │ │ ├── TimesNet_ETTh2.sh │ │ ├── TimesNet_ETTm1.sh │ │ ├── TimesNet_ETTm2.sh │ │ ├── Transformer_ETTh1.sh │ │ ├── Transformer_ETTh2.sh │ │ ├── Transformer_ETTm1.sh │ │ ├── Transformer_ETTm2.sh │ │ ├── WPMixer_ETTh1.sh │ │ ├── WPMixer_ETTh2.sh │ │ ├── WPMixer_ETTm1.sh │ │ ├── WPMixer_ETTm2.sh │ │ └── iTransformer_ETTh2.sh │ ├── Exchange_script │ │ ├── Autoformer.sh │ │ ├── Crossformer.sh │ │ ├── FiLM.sh │ │ ├── Koopa.sh │ │ ├── MICN.sh │ │ ├── Mamba.sh │ │ ├── Nonstationary_Transformer.sh │ │ ├── PatchTST.sh │ │ ├── Pyraformer.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh │ ├── ILI_script │ │ ├── Autoformer.sh │ │ ├── Crossformer.sh │ │ ├── FiLM.sh │ │ ├── Koopa.sh │ │ ├── MICN.sh │ │ ├── Nonstationary_Transformer.sh │ │ ├── PatchTST.sh │ │ ├── TimesNet.sh │ │ └── Transformer.sh │ ├── Mamba_all.sh │ ├── Traffic_script │ │ ├── Autoformer.sh │ │ ├── Crossformer.sh │ │ ├── FiLM.sh │ │ ├── Koopa.sh │ │ ├── MICN.sh │ │ ├── Mamba.sh │ │ ├── MultiPatchFormer.sh │ │ ├── Nonstationary_Transformer.sh │ │ ├── PatchTST.sh │ │ ├── Pyraformer.sh │ │ ├── SegRNN.sh │ │ ├── TSMixer.sh │ │ ├── TimeMixer.sh │ │ ├── TimeXer.sh │ │ ├── TimesNet.sh │ │ ├── Transformer.sh │ │ ├── WPMixer.sh │ │ └── iTransformer.sh │ └── Weather_script │ │ ├── Autoformer.sh │ │ ├── Crossformer.sh │ │ ├── FiLM.sh │ │ ├── MICN.sh │ │ ├── Mamba.sh │ │ ├── MultiPatchFormer.sh │ │ ├── Nonstationary_Transformer.sh │ │ ├── PatchTST.sh │ │ ├── Pyraformer.sh │ │ ├── SegRNN.sh │ │ ├── TSMixer.sh │ │ ├── TimeMixer.sh │ │ ├── TimeXer.sh │ │ ├── TimesNet.sh │ │ ├── Transformer.sh │ │ ├── WPMixer.sh │ │ └── iTransformer.sh └── short_term_forecast │ ├── Autoformer_M4.sh │ ├── Crossformer_M4.sh │ ├── DLinear_M4.sh │ ├── ETSformer_M4.sh │ ├── FEDformer_M4.sh │ ├── FiLM_M4.sh │ ├── Informer_M4.sh │ ├── LightTS_M4.sh │ ├── MICN_M4.sh │ ├── Mamba_M4.sh │ ├── Nonstationary_Transformer_M4.sh │ ├── Pyraformer_M4.sh │ ├── Reformer_M4.sh │ ├── TSMixer_M4.sh │ ├── TimeMixer_M4.sh │ ├── TimesNet_M4.sh │ ├── Transformer_M4.sh │ └── iTransformer_M4.sh ├── tutorial ├── TimesNet_tutorial.ipynb ├── conv.png ├── dataset.png ├── fft.png └── result.png └── utils ├── ADFtest.py ├── __init__.py ├── augmentation.py ├── dtw.py ├── dtw_metric.py ├── losses.py ├── m4_summary.py ├── masking.py ├── metrics.py ├── print_args.py ├── timefeatures.py └── tools.py /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | ## Instructions for Contributing to TSlib 2 | 3 | Sincerely thanks to all the researchers who want to use or contribute to TSlib. 4 | 5 | Since our team may not have enough time to fix all the bugs and catch up with the latest model, your contribution is essential to this project. 6 | 7 | ### (1) Fix Bug 8 | 9 | You can directly propose a pull request and add detailed descriptions to the comment, such as [this pull request](https://github.com/thuml/Time-Series-Library/pull/498). 10 | 11 | ### (2) Add a new time series model 12 | 13 | Thanks to creative researchers, extensive great TS models are presented, which advance this community significantly. If you want to add your model to TSlib, here are some instructions: 14 | 15 | - Propose an issue to describe your model and give a link to your paper and official code. We will discuss whether your model is suitable for this library, such as [this issue](https://github.com/thuml/Time-Series-Library/issues/346). 16 | - Propose a pull request in a similar style as TSlib, which means adding an additional file to ./models and providing corresponding scripts for reproduction, such as [this pull request](https://github.com/thuml/Time-Series-Library/pull/446). 17 | 18 | Note: Given that there are a lot of TS models that have been proposed, we may not have enough time to judge which model can be a remarkable supplement to the current library. Thus, we decide ONLY to add the officially published paper to our library. Peer review can be a reliable criterion. 19 | 20 | Thanks again for your valuable contributions. 21 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 THUML @ Tsinghua University 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /data_provider/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /exp/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Time-Series-Library/85c08390b6ecc5a5c3bae33b3880b8bc3e413023/exp/__init__.py -------------------------------------------------------------------------------- /layers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Time-Series-Library/85c08390b6ecc5a5c3bae33b3880b8bc3e413023/layers/__init__.py -------------------------------------------------------------------------------- /models/Mamba.py: -------------------------------------------------------------------------------- 1 | import math 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | from mamba_ssm import Mamba 8 | 9 | from layers.Embed import DataEmbedding 10 | 11 | class Model(nn.Module): 12 | 13 | def __init__(self, configs): 14 | super(Model, self).__init__() 15 | self.task_name = configs.task_name 16 | self.pred_len = configs.pred_len 17 | 18 | self.d_inner = configs.d_model * configs.expand 19 | self.dt_rank = math.ceil(configs.d_model / 16) # TODO implement "auto" 20 | 21 | self.embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq, configs.dropout) 22 | 23 | self.mamba = Mamba( 24 | d_model = configs.d_model, 25 | d_state = configs.d_ff, 26 | d_conv = configs.d_conv, 27 | expand = configs.expand, 28 | ) 29 | 30 | self.out_layer = nn.Linear(configs.d_model, configs.c_out, bias=False) 31 | 32 | def forecast(self, x_enc, x_mark_enc): 33 | mean_enc = x_enc.mean(1, keepdim=True).detach() 34 | x_enc = x_enc - mean_enc 35 | std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() 36 | x_enc = x_enc / std_enc 37 | 38 | x = self.embedding(x_enc, x_mark_enc) 39 | x = self.mamba(x) 40 | x_out = self.out_layer(x) 41 | 42 | x_out = x_out * std_enc + mean_enc 43 | return x_out 44 | 45 | def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): 46 | if self.task_name in ['short_term_forecast', 'long_term_forecast']: 47 | x_out = self.forecast(x_enc, x_mark_enc) 48 | return x_out[:, -self.pred_len:, :] 49 | 50 | # other tasks not implemented -------------------------------------------------------------------------------- /models/TSMixer.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | class ResBlock(nn.Module): 5 | def __init__(self, configs): 6 | super(ResBlock, self).__init__() 7 | 8 | self.temporal = nn.Sequential( 9 | nn.Linear(configs.seq_len, configs.d_model), 10 | nn.ReLU(), 11 | nn.Linear(configs.d_model, configs.seq_len), 12 | nn.Dropout(configs.dropout) 13 | ) 14 | 15 | self.channel = nn.Sequential( 16 | nn.Linear(configs.enc_in, configs.d_model), 17 | nn.ReLU(), 18 | nn.Linear(configs.d_model, configs.enc_in), 19 | nn.Dropout(configs.dropout) 20 | ) 21 | 22 | def forward(self, x): 23 | # x: [B, L, D] 24 | x = x + self.temporal(x.transpose(1, 2)).transpose(1, 2) 25 | x = x + self.channel(x) 26 | 27 | return x 28 | 29 | 30 | class Model(nn.Module): 31 | def __init__(self, configs): 32 | super(Model, self).__init__() 33 | self.task_name = configs.task_name 34 | self.layer = configs.e_layers 35 | self.model = nn.ModuleList([ResBlock(configs) 36 | for _ in range(configs.e_layers)]) 37 | self.pred_len = configs.pred_len 38 | self.projection = nn.Linear(configs.seq_len, configs.pred_len) 39 | 40 | def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): 41 | 42 | # x: [B, L, D] 43 | for i in range(self.layer): 44 | x_enc = self.model[i](x_enc) 45 | enc_out = self.projection(x_enc.transpose(1, 2)).transpose(1, 2) 46 | 47 | return enc_out 48 | 49 | def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): 50 | if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': 51 | dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec) 52 | return dec_out[:, -self.pred_len:, :] # [B, L, D] 53 | else: 54 | raise ValueError('Only forecast tasks implemented yet') 55 | -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Time-Series-Library/85c08390b6ecc5a5c3bae33b3880b8bc3e413023/models/__init__.py -------------------------------------------------------------------------------- /pic/dataset.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Time-Series-Library/85c08390b6ecc5a5c3bae33b3880b8bc3e413023/pic/dataset.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | einops==0.8.0 2 | local-attention==1.9.14 3 | matplotlib==3.7.0 4 | numpy==1.23.5 5 | pandas==1.5.3 6 | patool==1.12 7 | reformer-pytorch==1.4.4 8 | scikit-learn==1.2.2 9 | scipy==1.10.1 10 | sktime==0.16.1 11 | sympy==1.11.1 12 | torch==1.7.1 13 | tqdm==4.64.1 14 | PyWavelets -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model Autoformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/Crossformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model Crossformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/DLinear.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model DLinear \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 100 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/ETSformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model ETSformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 100 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --d_layers 3 \ 17 | --enc_in 55 \ 18 | --c_out 55 \ 19 | --anomaly_ratio 1 \ 20 | --batch_size 128 \ 21 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/FEDformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model FEDformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/FiLM.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model FiLM \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 100 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 32 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/Informer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model Informer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/LightTS.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model LightTS \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/MICN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model MICN \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/Pyraformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model Pyraformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/Reformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model Reformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model TimesNet \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 8 \ 14 | --d_ff 16 \ 15 | --e_layers 1 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --top_k 3 \ 19 | --anomaly_ratio 1 \ 20 | --batch_size 128 \ 21 | --train_epochs 1 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model Transformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/MSL/iTransformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/MSL \ 7 | --model_id MSL \ 8 | --model iTransformer \ 9 | --data MSL \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 55 \ 17 | --c_out 55 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/PSM/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/PSM \ 7 | --model_id PSM \ 8 | --model Autoformer \ 9 | --data PSM \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/PSM/DLinear.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/PSM \ 7 | --model_id PSM \ 8 | --model DLinear \ 9 | --data PSM \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 100 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/PSM/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/PSM \ 7 | --model_id PSM \ 8 | --model TimesNet \ 9 | --data PSM \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 64 \ 14 | --d_ff 64 \ 15 | --e_layers 2 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --top_k 3 \ 19 | --anomaly_ratio 1 \ 20 | --batch_size 128 \ 21 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/PSM/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/PSM \ 7 | --model_id PSM \ 8 | --model Transformer \ 9 | --data PSM \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SMAP/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SMAP \ 7 | --model_id SMAP \ 8 | --model Autoformer \ 9 | --data SMAP \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SMAP/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SMAP \ 7 | --model_id SMAP \ 8 | --model TimesNet \ 9 | --data SMAP \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --top_k 3 \ 19 | --anomaly_ratio 1 \ 20 | --batch_size 128 \ 21 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SMAP/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SMAP \ 7 | --model_id SMAP \ 8 | --model Transformer \ 9 | --data SMAP \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 25 \ 17 | --c_out 25 \ 18 | --anomaly_ratio 1 \ 19 | --batch_size 128 \ 20 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SMD/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SMD \ 7 | --model_id SMD \ 8 | --model Autoformer \ 9 | --data SMD \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 38 \ 17 | --c_out 38 \ 18 | --anomaly_ratio 0.5 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SMD/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SMD \ 7 | --model_id SMD \ 8 | --model TimesNet \ 9 | --data SMD \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 64 \ 14 | --d_ff 64 \ 15 | --e_layers 2 \ 16 | --enc_in 38 \ 17 | --c_out 38 \ 18 | --top_k 5 \ 19 | --anomaly_ratio 0.5 \ 20 | --batch_size 128 \ 21 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SMD/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SMD \ 7 | --model_id SMD \ 8 | --model Transformer \ 9 | --data SMD \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 38 \ 17 | --c_out 38 \ 18 | --anomaly_ratio 0.5 \ 19 | --batch_size 128 \ 20 | --train_epochs 10 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SWAT/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SWaT \ 7 | --model_id SWAT \ 8 | --model Autoformer \ 9 | --data SWAT \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 51 \ 17 | --c_out 51 \ 18 | --top_k 3 \ 19 | --anomaly_ratio 1 \ 20 | --batch_size 128 \ 21 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/anomaly_detection/SWAT/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | python -u run.py \ 4 | --task_name anomaly_detection \ 5 | --is_training 1 \ 6 | --root_path ./dataset/SWaT \ 7 | --model_id SWAT \ 8 | --model Transformer \ 9 | --data SWAT \ 10 | --features M \ 11 | --seq_len 100 \ 12 | --pred_len 0 \ 13 | --d_model 128 \ 14 | --d_ff 128 \ 15 | --e_layers 3 \ 16 | --enc_in 51 \ 17 | --c_out 51 \ 18 | --top_k 3 \ 19 | --anomaly_ratio 1 \ 20 | --batch_size 128 \ 21 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/exogenous_forecast/ECL/TimeXer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=TimeXer 4 | des='Timexer-MS' 5 | 6 | 7 | python -u run.py \ 8 | --task_name long_term_forecast \ 9 | --is_training 1 \ 10 | --root_path ./dataset/electricity/ \ 11 | --data_path electricity.csv \ 12 | --model_id ECL_96_96 \ 13 | --model $model_name \ 14 | --data custom \ 15 | --features MS \ 16 | --seq_len 96 \ 17 | --label_len 48 \ 18 | --pred_len 96 \ 19 | --e_layers 1 \ 20 | --factor 3 \ 21 | --enc_in 321 \ 22 | --dec_in 321 \ 23 | --c_out 321 \ 24 | --des $des \ 25 | --batch_size 4 \ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/electricity/ \ 32 | --data_path electricity.csv \ 33 | --model_id ECL_96_192 \ 34 | --model $model_name \ 35 | --data custom \ 36 | --features MS \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 321 \ 43 | --dec_in 321 \ 44 | --c_out 321 \ 45 | --des $des \ 46 | --batch_size 32 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/electricity/ \ 53 | --data_path electricity.csv \ 54 | --model_id ECL_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features MS \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 321 \ 64 | --dec_in 321 \ 65 | --c_out 321 \ 66 | --des $des \ 67 | --batch_size 32 \ 68 | --itr 1 69 | 70 | python -u run.py \ 71 | --task_name long_term_forecast \ 72 | --is_training 1 \ 73 | --root_path ./dataset/electricity/ \ 74 | --data_path electricity.csv \ 75 | --model_id ECL_96_720 \ 76 | --model $model_name \ 77 | --data custom \ 78 | --features MS \ 79 | --seq_len 96 \ 80 | --label_len 48 \ 81 | --pred_len 720 \ 82 | --e_layers 3 \ 83 | --factor 3 \ 84 | --enc_in 321 \ 85 | --dec_in 321 \ 86 | --c_out 321 \ 87 | --des $des \ 88 | --d_model 512 \ 89 | --itr 1 90 | -------------------------------------------------------------------------------- /scripts/exogenous_forecast/ETTh1/TimeXer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=TimeXer 4 | des='Timexer-MS' 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTh1.csv \ 11 | --model_id ETTh1_96_96 \ 12 | --model $model_name \ 13 | --data ETTh1 \ 14 | --features MS \ 15 | --seq_len 96 \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 2 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --d_model 512 \ 24 | --d_ff 512 \ 25 | --des $des \ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/ETT-small/ \ 32 | --data_path ETTh1.csv \ 33 | --model_id ETTh1_96_192 \ 34 | --model $model_name \ 35 | --data ETTh1 \ 36 | --features MS \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --d_model 128 \ 46 | --d_ff 128 \ 47 | --batch_size 4 \ 48 | --des $des \ 49 | --itr 1 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/ETT-small/ \ 55 | --data_path ETTh1.csv \ 56 | --model_id ETTh1_96_336 \ 57 | --model $model_name \ 58 | --data ETTh1 \ 59 | --features MS \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 2 \ 64 | --factor 3 \ 65 | --enc_in 7 \ 66 | --dec_in 7 \ 67 | --c_out 7 \ 68 | --d_model 512 \ 69 | --d_ff 512 \ 70 | --batch_size 32 \ 71 | --des $des \ 72 | --itr 1 73 | 74 | python -u run.py \ 75 | --task_name long_term_forecast \ 76 | --is_training 1 \ 77 | --root_path ./dataset/ETT-small/ \ 78 | --data_path ETTh1.csv \ 79 | --model_id ETTh1_96_720 \ 80 | --model $model_name \ 81 | --data ETTh1 \ 82 | --features MS \ 83 | --seq_len 96 \ 84 | --label_len 48 \ 85 | --pred_len 720 \ 86 | --e_layers 2 \ 87 | --factor 3 \ 88 | --enc_in 7 \ 89 | --dec_in 7 \ 90 | --c_out 7 \ 91 | --d_model 512 \ 92 | --batch_size 128 \ 93 | --des $des \ 94 | --itr 1 95 | -------------------------------------------------------------------------------- /scripts/exogenous_forecast/ETTm1/TimeXer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=TimeXer 4 | des='Timexer-MS' 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTm1.csv \ 11 | --model_id ETTm1_96_96 \ 12 | --model $model_name \ 13 | --data ETTm1 \ 14 | --features MS \ 15 | --seq_len 96 \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --d_model 256 \ 24 | --batch_size 128 \ 25 | --des $des \ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/ETT-small/ \ 32 | --data_path ETTm1.csv \ 33 | --model_id ETTm1_96_192 \ 34 | --model $model_name \ 35 | --data ETTm1 \ 36 | --features MS \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --d_model 128 \ 46 | --batch_size 128 \ 47 | --des $des \ 48 | --itr 1 49 | 50 | python -u run.py \ 51 | --task_name long_term_forecast \ 52 | --is_training 1 \ 53 | --root_path ./dataset/ETT-small/ \ 54 | --data_path ETTm1.csv \ 55 | --model_id ETTm1_96_336 \ 56 | --model $model_name \ 57 | --data ETTm1 \ 58 | --features MS \ 59 | --seq_len 96 \ 60 | --label_len 48 \ 61 | --pred_len 336 \ 62 | --e_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --d_model 128 \ 68 | --batch_size 128 \ 69 | --des $des \ 70 | --itr 1 71 | 72 | python -u run.py \ 73 | --task_name long_term_forecast \ 74 | --is_training 1 \ 75 | --root_path ./dataset/ETT-small/ \ 76 | --data_path ETTm1.csv \ 77 | --model_id ETTm1_96_720 \ 78 | --model $model_name \ 79 | --data ETTm1 \ 80 | --features MS \ 81 | --seq_len 96 \ 82 | --label_len 48 \ 83 | --pred_len 720 \ 84 | --e_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 7 \ 87 | --dec_in 7 \ 88 | --c_out 7 \ 89 | --d_model 128 \ 90 | --batch_size 128 \ 91 | --des $des \ 92 | --itr 1 -------------------------------------------------------------------------------- /scripts/exogenous_forecast/ETTm2/TimeXer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=TimeXer 4 | des='Timexer-MS' 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTm2.csv \ 11 | --model_id ETTm2_96_96 \ 12 | --model $model_name \ 13 | --data ETTm2 \ 14 | --features MS \ 15 | --seq_len 96 \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --d_model 512 \ 24 | --batch_size 16 \ 25 | --des $des \ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/ETT-small/ \ 32 | --data_path ETTm2.csv \ 33 | --model_id ETTm2_96_192 \ 34 | --model $model_name \ 35 | --data ETTm2 \ 36 | --features MS \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --d_model 256 \ 46 | --batch_size 4 \ 47 | --des $des \ 48 | --itr 1 49 | 50 | python -u run.py \ 51 | --task_name long_term_forecast \ 52 | --is_training 1 \ 53 | --root_path ./dataset/ETT-small/ \ 54 | --data_path ETTm2.csv \ 55 | --model_id ETTm2_96_336 \ 56 | --model $model_name \ 57 | --data ETTm2 \ 58 | --features MS \ 59 | --seq_len 96 \ 60 | --label_len 48 \ 61 | --pred_len 336 \ 62 | --e_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --d_model 128 \ 68 | --batch_size 128 \ 69 | --des $des \ 70 | --itr 1 71 | 72 | python -u run.py \ 73 | --task_name long_term_forecast \ 74 | --is_training 1 \ 75 | --root_path ./dataset/ETT-small/ \ 76 | --data_path ETTm2.csv \ 77 | --model_id ETTm2_96_720 \ 78 | --model $model_name \ 79 | --data ETTm2 \ 80 | --features MS \ 81 | --seq_len 96 \ 82 | --label_len 48 \ 83 | --pred_len 720 \ 84 | --e_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 7 \ 87 | --dec_in 7 \ 88 | --c_out 7 \ 89 | --d_model 128 \ 90 | --batch_size 128 \ 91 | --des $des \ 92 | --itr 1 93 | -------------------------------------------------------------------------------- /scripts/exogenous_forecast/Weather/TimeXer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=3 2 | 3 | model_name=TimeXer 4 | des='Timexer-MS' 5 | 6 | 7 | python -u run.py \ 8 | --task_name long_term_forecast \ 9 | --is_training 1 \ 10 | --root_path ./dataset/weather/ \ 11 | --data_path weather.csv \ 12 | --model_id weather_96_96 \ 13 | --model $model_name \ 14 | --data custom \ 15 | --features MS \ 16 | --seq_len 96 \ 17 | --label_len 48 \ 18 | --pred_len 96 \ 19 | --e_layers 1 \ 20 | --factor 3 \ 21 | --enc_in 21 \ 22 | --dec_in 21 \ 23 | --c_out 21 \ 24 | --des $des \ 25 | --d_model 128 \ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/weather/ \ 32 | --data_path weather.csv \ 33 | --model_id weather_96_192 \ 34 | --model $model_name \ 35 | --data custom \ 36 | --features MS \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 21 \ 43 | --dec_in 21 \ 44 | --c_out 21 \ 45 | --des $des \ 46 | --d_model 128 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/weather/ \ 53 | --data_path weather.csv \ 54 | --model_id weather_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features MS \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 21 \ 64 | --dec_in 21 \ 65 | --c_out 21 \ 66 | --des $des \ 67 | --d_model 128 \ 68 | --itr 1 69 | 70 | python -u run.py \ 71 | --task_name long_term_forecast \ 72 | --is_training 1 \ 73 | --root_path ./dataset/weather/ \ 74 | --data_path weather.csv \ 75 | --model_id weather_96_720 \ 76 | --model $model_name \ 77 | --data custom \ 78 | --features MS \ 79 | --seq_len 96 \ 80 | --label_len 48 \ 81 | --pred_len 720 \ 82 | --e_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 21 \ 85 | --dec_in 21 \ 86 | --c_out 21 \ 87 | --des $des \ 88 | --d_model 128 \ 89 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/AugmentSample/Classification/PatchTST.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=PatchTST 4 | 5 | for aug in jitter scaling permutation magwarp timewarp windowslice windowwarp rotation spawner dtwwarp shapedtwwarp wdba discdtw discsdtw 6 | do 7 | echo using augmentation: ${aug} 8 | 9 | python -u run.py \ 10 | --task_name classification \ 11 | --is_training 1 \ 12 | --root_path ./dataset/EthanolConcentration/ \ 13 | --model_id EthanolConcentration \ 14 | --model $model_name \ 15 | --data UEA \ 16 | --e_layers 3 \ 17 | --batch_size 16 \ 18 | --d_model 128 \ 19 | --d_ff 256 \ 20 | --top_k 3 \ 21 | --des 'Exp' \ 22 | --itr 1 \ 23 | --learning_rate 0.001 \ 24 | --train_epochs 100 \ 25 | --patience 10 \ 26 | --augmentation_ratio 1 \ 27 | --${aug} 28 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/AugmentSample/Forecasting/PatchTST.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=PatchTST 4 | for aug in jitter scaling permutation magwarp timewarp windowslice windowwarp rotation spawner dtwwarp shapedtwwarp discdtw discsdtw 5 | do 6 | for pred_len in 96 192 336 720 7 | do 8 | echo using augmentation: ${aug} 9 | 10 | python -u run.py \ 11 | --task_name long_term_forecast \ 12 | --is_training 1 \ 13 | --root_path ./dataset/exchange_rate/ \ 14 | --data_path exchange_rate.csv \ 15 | --model_id Exchange_96_${pred_len} \ 16 | --model $model_name \ 17 | --data custom \ 18 | --features M \ 19 | --seq_len 96 \ 20 | --label_len 48 \ 21 | --pred_len ${pred_len} \ 22 | --e_layers 2 \ 23 | --d_layers 1 \ 24 | --factor 3 \ 25 | --enc_in 8 \ 26 | --dec_in 8 \ 27 | --c_out 8 \ 28 | --des 'Exp' \ 29 | --itr 1 \ 30 | --augmentation_ratio 1 \ 31 | --${aug} 32 | done 33 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/DLinear.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=DLinear 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/ETSformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=ETSformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 2 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 2 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 2 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 2 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/FEDformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=FEDformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 88 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/FiLM.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=3 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features S \ 14 | --seq_len 192 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --batch_size 4 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/electricity/ \ 31 | --data_path electricity.csv \ 32 | --model_id ECL_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features S \ 36 | --seq_len 192 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 321 \ 43 | --dec_in 321 \ 44 | --c_out 321 \ 45 | --des 'Exp' \ 46 | --batch_size 4 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/electricity/ \ 53 | --data_path electricity.csv \ 54 | --model_id ECL_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features S \ 58 | --seq_len 192 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 321 \ 65 | --dec_in 321 \ 66 | --c_out 321 \ 67 | --des 'Exp' \ 68 | --batch_size 4 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/electricity/ \ 75 | --data_path electricity.csv \ 76 | --model_id ECL_96_720 \ 77 | --model $model_name \ 78 | --data custom \ 79 | --features S \ 80 | --seq_len 192 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 321 \ 87 | --dec_in 321 \ 88 | --c_out 321 \ 89 | --des 'Exp' \ 90 | --batch_size 4 \ 91 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Informer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Informer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Koopa.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_48 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 321 \ 20 | --dec_in 321 \ 21 | --c_out 321 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_192_96 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 321 \ 41 | --dec_in 321 \ 42 | --c_out 321 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_288_144 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 321 \ 62 | --dec_in 321 \ 63 | --c_out 321 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_384_192 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 321 \ 83 | --dec_in 321 \ 84 | --c_out 321 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/LightTS.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=LightTS 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Mamba.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | 3 | for pred_len in 96 192 336 720 4 | # for pred_len in 336 720 5 | do 6 | 7 | python -u run.py \ 8 | --task_name long_term_forecast \ 9 | --is_training 1 \ 10 | --root_path ./dataset/electricity/ \ 11 | --data_path electricity.csv \ 12 | --model_id ECL_$pred_len'_'$pred_len \ 13 | --model $model_name \ 14 | --data custom \ 15 | --features M \ 16 | --seq_len $pred_len \ 17 | --label_len 48 \ 18 | --pred_len $pred_len \ 19 | --e_layers 2 \ 20 | --d_layers 1 \ 21 | --enc_in 321 \ 22 | --expand 2 \ 23 | --d_ff 16 \ 24 | --d_conv 4 \ 25 | --c_out 321 \ 26 | --d_model 128 \ 27 | --des 'Exp' \ 28 | --itr 1 \ 29 | 30 | done 31 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/PatchTST.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=PatchTST 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --batch_size 16 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/electricity/ \ 31 | --data_path electricity.csv \ 32 | --model_id ECL_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 321 \ 43 | --dec_in 321 \ 44 | --c_out 321 \ 45 | --des 'Exp' \ 46 | --batch_size 16 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/electricity/ \ 53 | --data_path electricity.csv \ 54 | --model_id ECL_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 321 \ 65 | --dec_in 321 \ 66 | --c_out 321 \ 67 | --des 'Exp' \ 68 | --batch_size 16 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/electricity/ \ 75 | --data_path electricity.csv \ 76 | --model_id ECL_96_720 \ 77 | --model $model_name \ 78 | --data custom \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 321 \ 87 | --dec_in 321 \ 88 | --c_out 321 \ 89 | --des 'Exp' \ 90 | --batch_size 16 \ 91 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Pyraformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Reformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Reformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 321 \ 21 | --dec_in 321 \ 22 | --c_out 321 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/SegRNN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/electricity/ \ 12 | --data_path electricity.csv \ 13 | --model_id ECL_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data custom \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 24 \ 20 | --enc_in 321 \ 21 | --d_model 512 \ 22 | --dropout 0 \ 23 | --learning_rate 0.001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | 28 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/TimeXer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimeXer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 4 \ 18 | --factor 3 \ 19 | --enc_in 321 \ 20 | --dec_in 321 \ 21 | --c_out 321 \ 22 | --des 'Exp' \ 23 | --d_ff 512 \ 24 | --batch_size 4 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/electricity/ \ 31 | --data_path electricity.csv \ 32 | --model_id ECL_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 3 \ 40 | --factor 3 \ 41 | --enc_in 321 \ 42 | --dec_in 321 \ 43 | --c_out 321 \ 44 | --des 'Exp' \ 45 | --batch_size 4 \ 46 | --itr 1 47 | 48 | python -u run.py \ 49 | --task_name long_term_forecast \ 50 | --is_training 1 \ 51 | --root_path ./dataset/electricity/ \ 52 | --data_path electricity.csv \ 53 | --model_id ECL_96_336 \ 54 | --model $model_name \ 55 | --data custom \ 56 | --features M \ 57 | --seq_len 96 \ 58 | --label_len 48 \ 59 | --pred_len 336 \ 60 | --e_layers 4 \ 61 | --factor 3 \ 62 | --enc_in 321 \ 63 | --dec_in 321 \ 64 | --c_out 321 \ 65 | --des 'Exp' \ 66 | --batch_size 4 \ 67 | --itr 1 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/electricity/ \ 73 | --data_path electricity.csv \ 74 | --model_id ECL_96_720 \ 75 | --model $model_name \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 96 \ 79 | --label_len 48 \ 80 | --pred_len 720 \ 81 | --e_layers 3 \ 82 | --factor 3 \ 83 | --enc_in 321 \ 84 | --dec_in 321 \ 85 | --c_out 321 \ 86 | --des 'Exp' \ 87 | --batch_size 4 \ 88 | --itr 1 89 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/electricity/ \ 9 | --data_path electricity.csv \ 10 | --model_id ECL_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features S \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 1 \ 21 | --dec_in 1 \ 22 | --c_out 1 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/electricity/ \ 30 | --data_path electricity.csv \ 31 | --model_id ECL_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features S \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 1 \ 42 | --dec_in 1 \ 43 | --c_out 1 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/electricity/ \ 51 | --data_path electricity.csv \ 52 | --model_id ECL_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features S \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 1 \ 63 | --dec_in 1 \ 64 | --c_out 1 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/electricity/ \ 72 | --data_path electricity.csv \ 73 | --model_id ECL_96_720 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features S \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 1 \ 84 | --dec_in 1 \ 85 | --c_out 1 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ECL_script/WPMixer.sh: -------------------------------------------------------------------------------- 1 | 2 | export CUDA_VISIBLE_DEVICES=0 3 | 4 | # Model name 5 | model_name=WPMixer 6 | 7 | # Datasets and prediction lengths 8 | dataset=electricity 9 | seq_lens=(512 512 512 512) 10 | pred_lens=(96 192 336 720) 11 | learning_rates=(0.00328086 0.000493286 0.002505375 0.001977516) 12 | batches=(32 32 32 32) 13 | epochs=(100 100 100 100) 14 | dropouts=(0.1 0.1 0.2 0.1) 15 | patch_lens=(16 16 16 16) 16 | lradjs=(type3 type3 type3 type3) 17 | d_models=(32 32 32 32) 18 | patiences=(12 12 12 12) 19 | 20 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 21 | wavelets=(sym3 coif5 sym4 db2) 22 | levels=(2 3 1 2) 23 | tfactors=(3 7 5 7) 24 | dfactors=(5 5 7 8) 25 | strides=(8 8 8 8) 26 | 27 | # Loop over datasets and prediction lengths 28 | for i in "${!pred_lens[@]}"; do 29 | python -u run.py \ 30 | --is_training 1 \ 31 | --root_path ./data/electricity/ \ 32 | --data_path electricity.csv \ 33 | --model_id wpmixer \ 34 | --model $model_name \ 35 | --task_name long_term_forecast \ 36 | --data $dataset \ 37 | --seq_len ${seq_lens[$i]} \ 38 | --pred_len ${pred_lens[$i]} \ 39 | --label_len 0 \ 40 | --d_model ${d_models[$i]} \ 41 | --patch_len ${patch_lens[$i]} \ 42 | --batch_size ${batches[$i]} \ 43 | --learning_rate ${learning_rates[$i]} \ 44 | --lradj ${lradjs[$i]} \ 45 | --dropout ${dropouts[$i]} \ 46 | --patience ${patiences[$i]} \ 47 | --train_epochs ${epochs[$i]} \ 48 | --use_amp 49 | done 50 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Autoformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Autoformer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_96_192 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_96_336 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_96_720 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Autoformer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_96 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm1.csv \ 31 | --model_id ETTm1_96_192 \ 32 | --model $model_name \ 33 | --data ETTm1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm1.csv \ 52 | --model_id ETTm1_96_336 \ 53 | --model $model_name \ 54 | --data ETTm1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm1.csv \ 73 | --model_id ETTm1_96_720 \ 74 | --model $model_name \ 75 | --data ETTm1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Autoformer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_96 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 1 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm2.csv \ 31 | --model_id ETTm2_96_192 \ 32 | --model $model_name \ 33 | --data ETTm2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 1 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm2.csv \ 52 | --model_id ETTm2_96_336 \ 53 | --model $model_name \ 54 | --data ETTm2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 1 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm2.csv \ 73 | --model_id ETTm2_96_720 \ 74 | --model $model_name \ 75 | --data ETTm2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 1 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Crossformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Crossformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Crossformer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Crossformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_96_192 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_96_336 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_96_720 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Crossformer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Crossformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_96 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTm1.csv \ 30 | --model_id ETTm1_96_192 \ 31 | --model $model_name \ 32 | --data ETTm1 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --enc_in 7 \ 40 | --dec_in 7 \ 41 | --c_out 7 \ 42 | --des 'Exp' \ 43 | --itr 1 44 | 45 | python -u run.py \ 46 | --task_name long_term_forecast \ 47 | --is_training 1 \ 48 | --root_path ./dataset/ETT-small/ \ 49 | --data_path ETTm1.csv \ 50 | --model_id ETTm1_96_336 \ 51 | --model $model_name \ 52 | --data ETTm1 \ 53 | --features M \ 54 | --seq_len 96 \ 55 | --label_len 48 \ 56 | --pred_len 336 \ 57 | --e_layers 2 \ 58 | --d_layers 1 \ 59 | --enc_in 7 \ 60 | --dec_in 7 \ 61 | --c_out 7 \ 62 | --des 'Exp' \ 63 | --itr 1 64 | 65 | python -u run.py \ 66 | --task_name long_term_forecast \ 67 | --is_training 1 \ 68 | --root_path ./dataset/ETT-small/ \ 69 | --data_path ETTm1.csv \ 70 | --model_id ETTm1_96_720 \ 71 | --model $model_name \ 72 | --data ETTm1 \ 73 | --features M \ 74 | --seq_len 96 \ 75 | --label_len 48 \ 76 | --pred_len 720 \ 77 | --e_layers 2 \ 78 | --d_layers 1 \ 79 | --enc_in 7 \ 80 | --dec_in 7 \ 81 | --c_out 7 \ 82 | --des 'Exp' \ 83 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Crossformer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 2 | 3 | model_name=Crossformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_96 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 1 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm2.csv \ 31 | --model_id ETTm2_96_192 \ 32 | --model $model_name \ 33 | --data ETTm2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 1 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm2.csv \ 52 | --model_id ETTm2_96_336 \ 53 | --model $model_name \ 54 | --data ETTm2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 1 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm2.csv \ 73 | --model_id ETTm2_96_720 \ 74 | --model $model_name \ 75 | --data ETTm2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 1 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/DLinear_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=DLinear 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/ETSformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=ETSformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 2 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 2 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 2 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 2 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/FEDformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=FEDformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/FiLM_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 336 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 336 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 336 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 336 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/FiLM_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 168 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_96_192 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 168 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_96_336 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 168 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_96_720 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 168 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/FiLM_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_96 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 720 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm1.csv \ 31 | --model_id ETTm1_96_192 \ 32 | --model $model_name \ 33 | --data ETTm1 \ 34 | --features M \ 35 | --seq_len 720 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm1.csv \ 52 | --model_id ETTm1_96_336 \ 53 | --model $model_name \ 54 | --data ETTm1 \ 55 | --features M \ 56 | --seq_len 720 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm1.csv \ 73 | --model_id ETTm1_96_720 \ 74 | --model $model_name \ 75 | --data ETTm1 \ 76 | --features M \ 77 | --seq_len 720 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/FiLM_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=3 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_96 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 720 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm2.csv \ 31 | --model_id ETTm2_96_192 \ 32 | --model $model_name \ 33 | --data ETTm2 \ 34 | --features M \ 35 | --seq_len 720 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm2.csv \ 52 | --model_id ETTm2_96_336 \ 53 | --model $model_name \ 54 | --data ETTm2 \ 55 | --features M \ 56 | --seq_len 720 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm2.csv \ 73 | --model_id ETTm2_96_720 \ 74 | --model $model_name \ 75 | --data ETTm2 \ 76 | --features M \ 77 | --seq_len 720 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Informer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Informer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Koopa_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_48 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_192_96 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_288_144 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_384_192 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Koopa_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_48 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_192_96 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_288_144 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_384_192 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Koopa_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_48 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm1.csv \ 31 | --model_id ETTm1_192_96 \ 32 | --model $model_name \ 33 | --data ETTm1 \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm1.csv \ 52 | --model_id ETTm1_288_144 \ 53 | --model $model_name \ 54 | --data ETTm1 \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm1.csv \ 73 | --model_id ETTm1_384_192 \ 74 | --model $model_name \ 75 | --data ETTm1 \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Koopa_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_48 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm2.csv \ 31 | --model_id ETTm2_192_96 \ 32 | --model $model_name \ 33 | --data ETTm2 \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm2.csv \ 52 | --model_id ETTm2_288_144 \ 53 | --model $model_name \ 54 | --data ETTm2 \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm2.csv \ 73 | --model_id ETTm2_384_192 \ 74 | --model $model_name \ 75 | --data ETTm2 \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/LightTS_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=LightTS 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/MICN_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=MICN 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 96 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 96 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 96 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 96 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 88 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/MICN_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=MICN 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 96 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_96_192 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 96 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_96_336 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 96 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_96_720 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 96 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 88 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/MICN_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=MICN 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_96 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 96 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --top_k 5 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/ETT-small/ \ 31 | --data_path ETTm1.csv \ 32 | --model_id ETTm1_96_192 \ 33 | --model $model_name \ 34 | --data ETTm1 \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 96 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --des 'Exp' \ 46 | --top_k 5 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/ETT-small/ \ 53 | --data_path ETTm1.csv \ 54 | --model_id ETTm1_96_336 \ 55 | --model $model_name \ 56 | --data ETTm1 \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 96 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --des 'Exp' \ 68 | --top_k 5 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/ETT-small/ \ 75 | --data_path ETTm1.csv \ 76 | --model_id ETTm1_96_720 \ 77 | --model $model_name \ 78 | --data ETTm1 \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 96 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 7 \ 87 | --dec_in 7 \ 88 | --c_out 7 \ 89 | --des 'Exp' \ 90 | --top_k 5 \ 91 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/MICN_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=MICN 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_96 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 96 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --top_k 5 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/ETT-small/ \ 31 | --data_path ETTm2.csv \ 32 | --model_id ETTm2_96_192 \ 33 | --model $model_name \ 34 | --data ETTm2 \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 96 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --des 'Exp' \ 46 | --top_k 5 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/ETT-small/ \ 53 | --data_path ETTm2.csv \ 54 | --model_id ETTm2_96_336 \ 55 | --model $model_name \ 56 | --data ETTm2 \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 96 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --des 'Exp' \ 68 | --top_k 5 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/ETT-small/ \ 75 | --data_path ETTm2.csv \ 76 | --model_id ETTm2_96_720 \ 77 | --model $model_name \ 78 | --data ETTm2 \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 96 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 7 \ 87 | --dec_in 7 \ 88 | --c_out 7 \ 89 | --des 'Exp' \ 90 | --top_k 5 \ 91 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/MambaSimple_ETTh1.sh: -------------------------------------------------------------------------------- 1 | model_name=MambaSimple 2 | 3 | for pred_len in 96 192 336 720 4 | do 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTh1.csv \ 11 | --model_id ETTh1_$pred_len'_'$pred_len \ 12 | --model $model_name \ 13 | --data ETTh1 \ 14 | --features M \ 15 | --seq_len $pred_len \ 16 | --label_len 48 \ 17 | --pred_len $pred_len \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --enc_in 7 \ 21 | --expand 2 \ 22 | --d_ff 16 \ 23 | --d_conv 4 \ 24 | --c_out 7 \ 25 | --d_model 128 \ 26 | --des 'Exp' \ 27 | --itr 1 \ 28 | 29 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Mamba_ETT_all.sh: -------------------------------------------------------------------------------- 1 | ./scripts/long_term_forecast/ETT_script/Mamba_ETTh1.sh | tee mamba_ett.txt 2 | ./scripts/long_term_forecast/ETT_script/Mamba_ETTh2.sh | tee mamba_ett.txt -a 3 | ./scripts/long_term_forecast/ETT_script/Mamba_ETTm1.sh | tee mamba_ett.txt -a 4 | ./scripts/long_term_forecast/ETT_script/Mamba_ETTm2.sh | tee mamba_ett.txt -a -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Mamba_ETTh1.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | for pred_len in 96 192 336 720 3 | do 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_$pred_len'_'$pred_len \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len $pred_len \ 15 | --label_len 48 \ 16 | --pred_len $pred_len \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --enc_in 7 \ 20 | --expand 2 \ 21 | --d_ff 16 \ 22 | --d_conv 4 \ 23 | --c_out 7 \ 24 | --d_model 128 \ 25 | --des 'Exp' \ 26 | --itr 1 \ 27 | 28 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Mamba_ETTh2.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | 3 | for pred_len in 96 192 336 720 4 | do 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTh2.csv \ 11 | --model_id ETTh2_$pred_len'_'$pred_len \ 12 | --model $model_name \ 13 | --data ETTh2 \ 14 | --features M \ 15 | --seq_len $pred_len \ 16 | --label_len 48 \ 17 | --pred_len $pred_len \ 18 | --e_layers 2 \ 19 | --enc_in 7 \ 20 | --expand 2 \ 21 | --d_ff 16 \ 22 | --d_conv 4 \ 23 | --c_out 7 \ 24 | --d_model 128 \ 25 | --des 'Exp' \ 26 | --itr 1 \ 27 | 28 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Mamba_ETTm1.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | 3 | for pred_len in 96 192 336 720 4 | do 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTm1.csv \ 11 | --model_id ETTm1_$pred_len'_'$pred_len \ 12 | --model $model_name \ 13 | --data ETTm1 \ 14 | --features M \ 15 | --seq_len $pred_len \ 16 | --label_len 48 \ 17 | --pred_len $pred_len \ 18 | --e_layers 2 \ 19 | --enc_in 7 \ 20 | --expand 2 \ 21 | --d_ff 16 \ 22 | --d_conv 4 \ 23 | --c_out 7 \ 24 | --d_model 128 \ 25 | --des 'Exp' \ 26 | --itr 1 \ 27 | 28 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Mamba_ETTm2.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | 3 | for pred_len in 96 192 336 720 4 | do 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/ETT-small/ \ 10 | --data_path ETTm2.csv \ 11 | --model_id ETTm2_$pred_len'_'$pred_len \ 12 | --model $model_name \ 13 | --data ETTm2 \ 14 | --features M \ 15 | --seq_len $pred_len \ 16 | --label_len 48 \ 17 | --pred_len $pred_len \ 18 | --e_layers 2 \ 19 | --enc_in 7 \ 20 | --expand 2 \ 21 | --d_ff 16 \ 22 | --d_conv 4 \ 23 | --c_out 7 \ 24 | --d_model 128 \ 25 | --des 'Exp' \ 26 | --itr 1 \ 27 | 28 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/MultiPatchFormer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=MultiPatchFormer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 1 \ 18 | --enc_in 7 \ 19 | --dec_in 7 \ 20 | --c_out 7 \ 21 | --d_model 256 \ 22 | --d_ff 512 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/ETT-small/ \ 31 | --data_path ETTh1.csv \ 32 | --model_id ETTh1_96_192 \ 33 | --model $model_name \ 34 | --data ETTh1 \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 1 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --d_model 256 \ 44 | --d_ff 512 \ 45 | --des 'Exp' \ 46 | --itr 1 47 | 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/ETT-small/ \ 53 | --data_path ETTh1.csv \ 54 | --model_id ETTh1_96_336 \ 55 | --model $model_name \ 56 | --data ETTh1 \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 1 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --d_model 256 \ 66 | --d_ff 512 \ 67 | --des 'Exp' \ 68 | --itr 1 69 | 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/ETT-small/ \ 75 | --data_path ETTh1.csv \ 76 | --model_id ETTh1_96_720 \ 77 | --model $model_name \ 78 | --data ETTh1 \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 1 \ 84 | --enc_in 7 \ 85 | --dec_in 7 \ 86 | --c_out 7 \ 87 | --d_model 256 \ 88 | --d_ff 512 \ 89 | --des 'Exp' \ 90 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/PAttn_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=PAttn 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --factor 3 \ 18 | --enc_in 7 \ 19 | --dec_in 7 \ 20 | --c_out 7 \ 21 | --des 'Exp' \ 22 | --n_heads 2 \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTh1.csv \ 30 | --model_id ETTh1_96_192 \ 31 | --model $model_name \ 32 | --data ETTh1 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --factor 3 \ 38 | --enc_in 7 \ 39 | --dec_in 7 \ 40 | --c_out 7 \ 41 | --des 'Exp' \ 42 | --n_heads 8 \ 43 | --itr 1 44 | 45 | python -u run.py \ 46 | --task_name long_term_forecast \ 47 | --is_training 1 \ 48 | --root_path ./dataset/ETT-small/ \ 49 | --data_path ETTh1.csv \ 50 | --model_id ETTh1_96_336 \ 51 | --model $model_name \ 52 | --data ETTh1 \ 53 | --features M \ 54 | --seq_len 96 \ 55 | --label_len 48 \ 56 | --pred_len 336 \ 57 | --factor 3 \ 58 | --enc_in 7 \ 59 | --dec_in 7 \ 60 | --c_out 7 \ 61 | --des 'Exp' \ 62 | --n_heads 8 \ 63 | --itr 1 64 | 65 | python -u run.py \ 66 | --task_name long_term_forecast \ 67 | --is_training 1 \ 68 | --root_path ./dataset/ETT-small/ \ 69 | --data_path ETTh1.csv \ 70 | --model_id ETTh1_96_720 \ 71 | --model $model_name \ 72 | --data ETTh1 \ 73 | --features M \ 74 | --seq_len 96 \ 75 | --label_len 48 \ 76 | --pred_len 720 \ 77 | --factor 3 \ 78 | --enc_in 7 \ 79 | --dec_in 7 \ 80 | --c_out 7 \ 81 | --des 'Exp' \ 82 | --n_heads 16 \ 83 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/PatchTST_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=PatchTST 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 1 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --n_heads 2 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/ETT-small/ \ 31 | --data_path ETTh1.csv \ 32 | --model_id ETTh1_96_192 \ 33 | --model $model_name \ 34 | --data ETTh1 \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 1 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --des 'Exp' \ 46 | --n_heads 8 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/ETT-small/ \ 53 | --data_path ETTh1.csv \ 54 | --model_id ETTh1_96_336 \ 55 | --model $model_name \ 56 | --data ETTh1 \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 1 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --des 'Exp' \ 68 | --n_heads 8 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/ETT-small/ \ 75 | --data_path ETTh1.csv \ 76 | --model_id ETTh1_96_720 \ 77 | --model $model_name \ 78 | --data ETTh1 \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 1 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 7 \ 87 | --dec_in 7 \ 88 | --c_out 7 \ 89 | --des 'Exp' \ 90 | --n_heads 16 \ 91 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/PatchTST_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=PatchTST 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 3 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --n_heads 4 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/ETT-small/ \ 31 | --data_path ETTh2.csv \ 32 | --model_id ETTh2_96_192 \ 33 | --model $model_name \ 34 | --data ETTh2 \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 3 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --des 'Exp' \ 46 | --n_heads 4 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/ETT-small/ \ 53 | --data_path ETTh2.csv \ 54 | --model_id ETTh2_96_336 \ 55 | --model $model_name \ 56 | --data ETTh2 \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 3 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --des 'Exp' \ 68 | --n_heads 4 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/ETT-small/ \ 75 | --data_path ETTh2.csv \ 76 | --model_id ETTh2_96_720 \ 77 | --model $model_name \ 78 | --data ETTh2 \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 3 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 7 \ 87 | --dec_in 7 \ 88 | --c_out 7 \ 89 | --des 'Exp' \ 90 | --n_heads 4 \ 91 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Pyraformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Pyraformer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_96_192 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_96_336 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_96_720 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Pyraformer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_96 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm1.csv \ 31 | --model_id ETTm1_96_192 \ 32 | --model $model_name \ 33 | --data ETTm1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm1.csv \ 52 | --model_id ETTm1_96_336 \ 53 | --model $model_name \ 54 | --data ETTm1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm1.csv \ 73 | --model_id ETTm1_96_720 \ 74 | --model $model_name \ 75 | --data ETTm1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Pyraformer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_96 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 1 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm2.csv \ 31 | --model_id ETTm2_96_192 \ 32 | --model $model_name \ 33 | --data ETTm2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 1 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm2.csv \ 52 | --model_id ETTm2_96_336 \ 53 | --model $model_name \ 54 | --data ETTm2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 1 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm2.csv \ 73 | --model_id ETTm2_96_720 \ 74 | --model $model_name \ 75 | --data ETTm2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 1 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Reformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Reformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/SegRNN_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/ETT-small/ \ 12 | --data_path ETTh1.csv \ 13 | --model_id ETTh1_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data ETTh1 \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 24 \ 20 | --enc_in 7 \ 21 | --d_model 512 \ 22 | --dropout 0.5 \ 23 | --learning_rate 0.0001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/SegRNN_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/ETT-small/ \ 12 | --data_path ETTh2.csv \ 13 | --model_id ETTh2_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data ETTh2 \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 24 \ 20 | --enc_in 7 \ 21 | --d_model 512 \ 22 | --dropout 0.5 \ 23 | --learning_rate 0.0001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/SegRNN_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/ETT-small/ \ 12 | --data_path ETTm1.csv \ 13 | --model_id ETTm1_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data ETTm1 \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 48 \ 20 | --enc_in 7 \ 21 | --d_model 512 \ 22 | --dropout 0.5 \ 23 | --learning_rate 0.0001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/SegRNN_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/ETT-small/ \ 12 | --data_path ETTm2.csv \ 13 | --model_id ETTm2_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data ETTm2 \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 48 \ 20 | --enc_in 7 \ 21 | --d_model 512 \ 22 | --dropout 0.5 \ 23 | --learning_rate 0.0001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/TSMixer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | 2 | model_name=TSMixer 3 | 4 | python -u run.py \ 5 | --task_name long_term_forecast \ 6 | --is_training 1 \ 7 | --root_path ./dataset/ETT-small/ \ 8 | --data_path ETTh1.csv \ 9 | --model_id ETTh1_96_96 \ 10 | --model $model_name \ 11 | --data ETTh1 \ 12 | --features M \ 13 | --seq_len 96 \ 14 | --label_len 48 \ 15 | --pred_len 96 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTh1.csv \ 30 | --model_id ETTh1_96_192 \ 31 | --model $model_name \ 32 | --data ETTh1 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --itr 1 45 | 46 | python -u run.py \ 47 | --task_name long_term_forecast \ 48 | --is_training 1 \ 49 | --root_path ./dataset/ETT-small/ \ 50 | --data_path ETTh1.csv \ 51 | --model_id ETTh1_96_336 \ 52 | --model $model_name \ 53 | --data ETTh1 \ 54 | --features M \ 55 | --seq_len 96 \ 56 | --label_len 48 \ 57 | --pred_len 336 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --itr 1 66 | 67 | python -u run.py \ 68 | --task_name long_term_forecast \ 69 | --is_training 1 \ 70 | --root_path ./dataset/ETT-small/ \ 71 | --data_path ETTh1.csv \ 72 | --model_id ETTh1_96_720 \ 73 | --model $model_name \ 74 | --data ETTh1 \ 75 | --features M \ 76 | --seq_len 96 \ 77 | --label_len 48 \ 78 | --pred_len 720 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/TSMixer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | 2 | model_name=TSMixer 3 | 4 | python -u run.py \ 5 | --task_name long_term_forecast \ 6 | --is_training 1 \ 7 | --root_path ./dataset/ETT-small/ \ 8 | --data_path ETTh2.csv \ 9 | --model_id ETTh2_96_96 \ 10 | --model $model_name \ 11 | --data ETTh2 \ 12 | --features M \ 13 | --seq_len 96 \ 14 | --label_len 48 \ 15 | --pred_len 96 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTh2.csv \ 30 | --model_id ETTh2_96_192 \ 31 | --model $model_name \ 32 | --data ETTh2 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --itr 1 45 | 46 | python -u run.py \ 47 | --task_name long_term_forecast \ 48 | --is_training 1 \ 49 | --root_path ./dataset/ETT-small/ \ 50 | --data_path ETTh2.csv \ 51 | --model_id ETTh2_96_336 \ 52 | --model $model_name \ 53 | --data ETTh2 \ 54 | --features M \ 55 | --seq_len 96 \ 56 | --label_len 48 \ 57 | --pred_len 336 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --itr 1 66 | 67 | python -u run.py \ 68 | --task_name long_term_forecast \ 69 | --is_training 1 \ 70 | --root_path ./dataset/ETT-small/ \ 71 | --data_path ETTh2.csv \ 72 | --model_id ETTh2_96_720 \ 73 | --model $model_name \ 74 | --data ETTh2 \ 75 | --features M \ 76 | --seq_len 96 \ 77 | --label_len 48 \ 78 | --pred_len 720 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/TSMixer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | 2 | model_name=TSMixer 3 | 4 | python -u run.py \ 5 | --task_name long_term_forecast \ 6 | --is_training 1 \ 7 | --root_path ./dataset/ETT-small/ \ 8 | --data_path ETTm1.csv \ 9 | --model_id ETTm1_96_96 \ 10 | --model $model_name \ 11 | --data ETTm1 \ 12 | --features M \ 13 | --seq_len 96 \ 14 | --label_len 48 \ 15 | --pred_len 96 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTm1.csv \ 30 | --model_id ETTm1_96_192 \ 31 | --model $model_name \ 32 | --data ETTm1 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --itr 1 45 | 46 | python -u run.py \ 47 | --task_name long_term_forecast \ 48 | --is_training 1 \ 49 | --root_path ./dataset/ETT-small/ \ 50 | --data_path ETTm1.csv \ 51 | --model_id ETTm1_96_336 \ 52 | --model $model_name \ 53 | --data ETTm1 \ 54 | --features M \ 55 | --seq_len 96 \ 56 | --label_len 48 \ 57 | --pred_len 336 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --itr 1 66 | 67 | python -u run.py \ 68 | --task_name long_term_forecast \ 69 | --is_training 1 \ 70 | --root_path ./dataset/ETT-small/ \ 71 | --data_path ETTm1.csv \ 72 | --model_id ETTm1_96_720 \ 73 | --model $model_name \ 74 | --data ETTm1 \ 75 | --features M \ 76 | --seq_len 96 \ 77 | --label_len 48 \ 78 | --pred_len 720 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/TSMixer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | 2 | model_name=TSMixer 3 | 4 | python -u run.py \ 5 | --task_name long_term_forecast \ 6 | --is_training 1 \ 7 | --root_path ./dataset/ETT-small/ \ 8 | --data_path ETTm2.csv \ 9 | --model_id ETTm2_96_96 \ 10 | --model $model_name \ 11 | --data ETTm2 \ 12 | --features M \ 13 | --seq_len 96 \ 14 | --label_len 48 \ 15 | --pred_len 96 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 1 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTm2.csv \ 30 | --model_id ETTm2_96_192 \ 31 | --model $model_name \ 32 | --data ETTm2 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 1 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --itr 1 45 | 46 | python -u run.py \ 47 | --task_name long_term_forecast \ 48 | --is_training 1 \ 49 | --root_path ./dataset/ETT-small/ \ 50 | --data_path ETTm2.csv \ 51 | --model_id ETTm2_96_336 \ 52 | --model $model_name \ 53 | --data ETTm2 \ 54 | --features M \ 55 | --seq_len 96 \ 56 | --label_len 48 \ 57 | --pred_len 336 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 1 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --itr 1 66 | 67 | python -u run.py \ 68 | --task_name long_term_forecast \ 69 | --is_training 1 \ 70 | --root_path ./dataset/ETT-small/ \ 71 | --data_path ETTm2.csv \ 72 | --model_id ETTm2_96_720 \ 73 | --model $model_name \ 74 | --data ETTm2 \ 75 | --features M \ 76 | --seq_len 96 \ 77 | --label_len 48 \ 78 | --pred_len 720 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 1 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/TimeXer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=TimeXer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --d_model 256 \ 23 | --batch_size 4 \ 24 | --des 'exp' \ 25 | --itr 1 26 | 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/ETT-small/ \ 32 | --data_path ETTh1.csv \ 33 | --model_id ETTh1_96_192 \ 34 | --model $model_name \ 35 | --data ETTh1 \ 36 | --features M \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --factor 3 \ 42 | --enc_in 7 \ 43 | --dec_in 7 \ 44 | --c_out 7 \ 45 | --des 'Exp' \ 46 | --d_model 128 \ 47 | --batch_size 4 \ 48 | --itr 1 49 | 50 | python -u run.py \ 51 | --task_name long_term_forecast \ 52 | --is_training 1 \ 53 | --root_path ./dataset/ETT-small/ \ 54 | --data_path ETTh1.csv \ 55 | --model_id ETTh1_96_336 \ 56 | --model $model_name \ 57 | --data ETTh1 \ 58 | --features M \ 59 | --seq_len 96 \ 60 | --label_len 48 \ 61 | --pred_len 336 \ 62 | --e_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 7 \ 65 | --dec_in 7 \ 66 | --c_out 7 \ 67 | --des 'Exp' \ 68 | --d_model 512 \ 69 | --d_ff 1024 \ 70 | --batch_size 16 \ 71 | --itr 1 72 | 73 | python -u run.py \ 74 | --task_name long_term_forecast \ 75 | --is_training 1 \ 76 | --root_path ./dataset/ETT-small/ \ 77 | --data_path ETTh1.csv \ 78 | --model_id ETTh1_96_720 \ 79 | --model $model_name \ 80 | --data ETTh1 \ 81 | --features M \ 82 | --seq_len 96 \ 83 | --label_len 48 \ 84 | --pred_len 720 \ 85 | --e_layers 1 \ 86 | --factor 3 \ 87 | --enc_in 7 \ 88 | --dec_in 7 \ 89 | --c_out 7 \ 90 | --des 'Exp' \ 91 | --d_model 256 \ 92 | --d_ff 1024 \ 93 | --batch_size 16 \ 94 | --itr 1 95 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Transformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh1.csv \ 10 | --model_id ETTh1_96_96 \ 11 | --model $model_name \ 12 | --data ETTh1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh1.csv \ 31 | --model_id ETTh1_96_192 \ 32 | --model $model_name \ 33 | --data ETTh1 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh1.csv \ 52 | --model_id ETTh1_96_336 \ 53 | --model $model_name \ 54 | --data ETTh1 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh1.csv \ 73 | --model_id ETTh1_96_720 \ 74 | --model $model_name \ 75 | --data ETTh1 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Transformer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTh2.csv \ 10 | --model_id ETTh2_96_96 \ 11 | --model $model_name \ 12 | --data ETTh2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTh2.csv \ 31 | --model_id ETTh2_96_192 \ 32 | --model $model_name \ 33 | --data ETTh2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTh2.csv \ 52 | --model_id ETTh2_96_336 \ 53 | --model $model_name \ 54 | --data ETTh2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTh2.csv \ 73 | --model_id ETTh2_96_720 \ 74 | --model $model_name \ 75 | --data ETTh2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Transformer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm1.csv \ 10 | --model_id ETTm1_96_96 \ 11 | --model $model_name \ 12 | --data ETTm1 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --itr 1 24 | 25 | python -u run.py \ 26 | --task_name long_term_forecast \ 27 | --is_training 1 \ 28 | --root_path ./dataset/ETT-small/ \ 29 | --data_path ETTm1.csv \ 30 | --model_id ETTm1_96_192 \ 31 | --model $model_name \ 32 | --data ETTm1 \ 33 | --features M \ 34 | --seq_len 96 \ 35 | --label_len 48 \ 36 | --pred_len 192 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --enc_in 7 \ 40 | --dec_in 7 \ 41 | --c_out 7 \ 42 | --des 'Exp' \ 43 | --itr 1 44 | 45 | python -u run.py \ 46 | --task_name long_term_forecast \ 47 | --is_training 1 \ 48 | --root_path ./dataset/ETT-small/ \ 49 | --data_path ETTm1.csv \ 50 | --model_id ETTm1_96_336 \ 51 | --model $model_name \ 52 | --data ETTm1 \ 53 | --features M \ 54 | --seq_len 96 \ 55 | --label_len 48 \ 56 | --pred_len 336 \ 57 | --e_layers 2 \ 58 | --d_layers 1 \ 59 | --enc_in 7 \ 60 | --dec_in 7 \ 61 | --c_out 7 \ 62 | --des 'Exp' \ 63 | --itr 1 64 | 65 | python -u run.py \ 66 | --task_name long_term_forecast \ 67 | --is_training 1 \ 68 | --root_path ./dataset/ETT-small/ \ 69 | --data_path ETTm1.csv \ 70 | --model_id ETTm1_96_720 \ 71 | --model $model_name \ 72 | --data ETTm1 \ 73 | --features M \ 74 | --seq_len 96 \ 75 | --label_len 48 \ 76 | --pred_len 720 \ 77 | --e_layers 2 \ 78 | --d_layers 1 \ 79 | --enc_in 7 \ 80 | --dec_in 7 \ 81 | --c_out 7 \ 82 | --des 'Exp' \ 83 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/Transformer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/ETT-small/ \ 9 | --data_path ETTm2.csv \ 10 | --model_id ETTm2_96_96 \ 11 | --model $model_name \ 12 | --data ETTm2 \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 1 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/ETT-small/ \ 30 | --data_path ETTm2.csv \ 31 | --model_id ETTm2_96_192 \ 32 | --model $model_name \ 33 | --data ETTm2 \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 1 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/ETT-small/ \ 51 | --data_path ETTm2.csv \ 52 | --model_id ETTm2_96_336 \ 53 | --model $model_name \ 54 | --data ETTm2 \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 1 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/ETT-small/ \ 72 | --data_path ETTm2.csv \ 73 | --model_id ETTm2_96_720 \ 74 | --model $model_name \ 75 | --data ETTm2 \ 76 | --features M \ 77 | --seq_len 96 \ 78 | --label_len 48 \ 79 | --pred_len 720 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 1 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/WPMixer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | 2 | # Set the GPU to use 3 | export CUDA_VISIBLE_DEVICES=0 4 | 5 | # Model name 6 | model_name=WPMixer 7 | 8 | # Datasets and prediction lengths 9 | dataset=ETTh1 10 | seq_lens=(512 512 512 512) 11 | pred_lens=(96 192 336 720) 12 | learning_rates=(0.000242438 0.000201437 0.000132929 0.000239762) 13 | batches=(256 256 256 256) 14 | epochs=(30 30 30 30) 15 | dropouts=(0.4 0.05 0.0 0.2) 16 | patch_lens=(16 16 16 16) 17 | lradjs=(type3 type3 type3 type3) 18 | d_models=(256 256 256 128) 19 | patiences=(12 12 12 12) 20 | 21 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 22 | wavelets=(db2 db3 db2 db2) 23 | levels=(2 2 1 1) 24 | tfactors=(5 5 3 5) 25 | dfactors=(8 5 3 3) 26 | strides=(8 8 8 8) 27 | 28 | # Loop over datasets and prediction lengths 29 | for i in "${!pred_lens[@]}"; do 30 | python -u run.py \ 31 | --is_training 1 \ 32 | --root_path ./data/ETT/ \ 33 | --data_path ETTh1.csv \ 34 | --model_id wpmixer \ 35 | --model $model_name \ 36 | --task_name long_term_forecast \ 37 | --data $dataset \ 38 | --seq_len ${seq_lens[$i]} \ 39 | --pred_len ${pred_lens[$i]} \ 40 | --label_len 0 \ 41 | --d_model ${d_models[$i]} \ 42 | --patch_len ${patch_lens[$i]} \ 43 | --batch_size ${batches[$i]} \ 44 | --learning_rate ${learning_rates[$i]} \ 45 | --lradj ${lradjs[$i]} \ 46 | --dropout ${dropouts[$i]} \ 47 | --patience ${patiences[$i]} \ 48 | --train_epochs ${epochs[$i]} \ 49 | --use_amp 50 | done 51 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/WPMixer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | 2 | export CUDA_VISIBLE_DEVICES=0 3 | 4 | # Model name 5 | model_name=WPMixer 6 | 7 | # Datasets and prediction lengths 8 | dataset=ETTh2 9 | seq_lens=(512 512 512 512) 10 | pred_lens=(96 192 336 720) 11 | learning_rates=(0.000466278 0.000294929 0.000617476 0.000810205) 12 | batches=(256 256 256 256) 13 | epochs=(30 30 30 30) 14 | dropouts=(0.0 0.0 0.1 0.4) 15 | patch_lens=(16 16 16 16) 16 | lradjs=(type3 type3 type3 type3) 17 | d_models=(256 256 128 128) 18 | patiences=(12 12 12 12) 19 | 20 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 21 | wavelets=(db2 db2 db2 db2) 22 | levels=(2 3 5 5) 23 | tfactors=(5 3 5 5) 24 | dfactors=(5 8 3 5) 25 | strides=(8 8 8 8) 26 | 27 | # Loop over datasets and prediction lengths 28 | for i in "${!pred_lens[@]}"; do 29 | python -u run.py \ 30 | --is_training 1 \ 31 | --root_path ./data/ETT/ \ 32 | --data_path ETTh2.csv \ 33 | --model_id wpmixer \ 34 | --model $model_name \ 35 | --task_name long_term_forecast \ 36 | --data $dataset \ 37 | --seq_len ${seq_lens[$i]} \ 38 | --pred_len ${pred_lens[$i]} \ 39 | --label_len 0 \ 40 | --d_model ${d_models[$i]} \ 41 | --patch_len ${patch_lens[$i]} \ 42 | --batch_size ${batches[$i]} \ 43 | --learning_rate ${learning_rates[$i]} \ 44 | --lradj ${lradjs[$i]} \ 45 | --dropout ${dropouts[$i]} \ 46 | --patience ${patiences[$i]} \ 47 | --train_epochs ${epochs[$i]} \ 48 | --use_amp 49 | done 50 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/WPMixer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | 2 | export CUDA_VISIBLE_DEVICES=0 3 | 4 | # Model name 5 | model_name=WPMixer 6 | 7 | # Datasets and prediction lengths 8 | dataset=ETTm1 9 | seq_lens=(512 512 512 512) 10 | pred_lens=(96 192 336 720) 11 | learning_rates=(0.001277976 0.002415901 0.001594735 0.002011441) 12 | batches=(256 256 256 256) 13 | epochs=(80 80 80 80) 14 | dropouts=(0.4 0.4 0.4 0.4) 15 | patch_lens=(48 48 48 48) 16 | lradjs=(type3 type3 type3 type3) 17 | d_models=(256 128 256 128) 18 | patiences=(12 12 12 12) 19 | 20 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 21 | wavelets=(db2 db3 db5 db5) 22 | levels=(1 1 1 4) 23 | tfactors=(5 3 7 3) 24 | dfactors=(3 7 7 8) 25 | strides=(24 24 24 24) 26 | 27 | # Loop over datasets and prediction lengths 28 | for i in "${!pred_lens[@]}"; do 29 | python -u run.py \ 30 | --is_training 1 \ 31 | --root_path ./data/ETT/ \ 32 | --data_path ETTm1.csv \ 33 | --model_id wpmixer \ 34 | --model $model_name \ 35 | --task_name long_term_forecast \ 36 | --data $dataset \ 37 | --seq_len ${seq_lens[$i]} \ 38 | --pred_len ${pred_lens[$i]} \ 39 | --label_len 0 \ 40 | --d_model ${d_models[$i]} \ 41 | --patch_len ${patch_lens[$i]} \ 42 | --batch_size ${batches[$i]} \ 43 | --learning_rate ${learning_rates[$i]} \ 44 | --lradj ${lradjs[$i]} \ 45 | --dropout ${dropouts[$i]} \ 46 | --patience ${patiences[$i]} \ 47 | --train_epochs ${epochs[$i]} \ 48 | --use_amp 49 | done 50 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/ETT_script/WPMixer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | 2 | # Set the GPU to use 3 | export CUDA_VISIBLE_DEVICES=0 4 | 5 | # Model name 6 | model_name=WPMixer 7 | 8 | # Datasets and prediction lengths 9 | dataset=ETTm2 10 | seq_lens=(512 512 512 512) 11 | pred_lens=(96 192 336 720) 12 | learning_rates=(0.00076587 0.000275775 0.000234608 0.001039536) 13 | batches=(256 256 256 256) 14 | epochs=(80 80 80 80) 15 | dropouts=(0.4 0.2 0.4 0.4) 16 | patch_lens=(48 48 48 48) 17 | lradjs=(type3 type3 type3 type3) 18 | d_models=(256 256 256 256) 19 | patiences=(12 12 12 12) 20 | 21 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 22 | wavelets=(bior3.1 db2 db2 db2) 23 | levels=(1 1 1 1) 24 | tfactors=(3 3 3 3) 25 | dfactors=(8 7 5 8) 26 | strides=(24 24 24 24) 27 | 28 | # Loop over datasets and prediction lengths 29 | for i in "${!pred_lens[@]}"; do 30 | python -u run.py \ 31 | --is_training 1 \ 32 | --root_path ./data/ETT/ \ 33 | --data_path ETTm2.csv \ 34 | --model_id wpmixer \ 35 | --model $model_name \ 36 | --task_name long_term_forecast \ 37 | --data $dataset \ 38 | --seq_len ${seq_lens[$i]} \ 39 | --pred_len ${pred_lens[$i]} \ 40 | --label_len 0 \ 41 | --d_model ${d_models[$i]} \ 42 | --patch_len ${patch_lens[$i]} \ 43 | --batch_size ${batches[$i]} \ 44 | --learning_rate ${learning_rates[$i]} \ 45 | --lradj ${lradjs[$i]} \ 46 | --dropout ${dropouts[$i]} \ 47 | --patience ${patiences[$i]} \ 48 | --train_epochs ${epochs[$i]} \ 49 | --use_amp 50 | done 51 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 8 \ 21 | --dec_in 8 \ 22 | --c_out 8 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/exchange_rate/ \ 30 | --data_path exchange_rate.csv \ 31 | --model_id Exchange_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 8 \ 42 | --dec_in 8 \ 43 | --c_out 8 \ 44 | --des 'Exp' \ 45 | --itr 1 \ 46 | --train_epochs 1 47 | 48 | python -u run.py \ 49 | --task_name long_term_forecast \ 50 | --is_training 1 \ 51 | --root_path ./dataset/exchange_rate/ \ 52 | --data_path exchange_rate.csv \ 53 | --model_id Exchange_96_336 \ 54 | --model $model_name \ 55 | --data custom \ 56 | --features M \ 57 | --seq_len 96 \ 58 | --label_len 48 \ 59 | --pred_len 336 \ 60 | --e_layers 2 \ 61 | --d_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 8 \ 64 | --dec_in 8 \ 65 | --c_out 8 \ 66 | --des 'Exp' \ 67 | --itr 1 \ 68 | --train_epochs 1 69 | 70 | python -u run.py \ 71 | --task_name long_term_forecast \ 72 | --is_training 1 \ 73 | --root_path ./dataset/exchange_rate/ \ 74 | --data_path exchange_rate.csv \ 75 | --model_id Exchange_96_720 \ 76 | --model $model_name \ 77 | --data custom \ 78 | --features M \ 79 | --seq_len 96 \ 80 | --label_len 48 \ 81 | --pred_len 720 \ 82 | --e_layers 2 \ 83 | --d_layers 1 \ 84 | --factor 3 \ 85 | --enc_in 8 \ 86 | --dec_in 8 \ 87 | --c_out 8 \ 88 | --des 'Exp' \ 89 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/FiLM.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 384 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 8 \ 21 | --dec_in 8 \ 22 | --c_out 8 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/exchange_rate/ \ 32 | --data_path exchange_rate.csv \ 33 | --model_id Exchange_96_192 \ 34 | --model $model_name \ 35 | --data custom \ 36 | --features M \ 37 | --seq_len 384 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 8 \ 44 | --dec_in 8 \ 45 | --c_out 8 \ 46 | --des 'Exp' \ 47 | --itr 1 48 | 49 | 50 | python -u run.py \ 51 | --task_name long_term_forecast \ 52 | --is_training 1 \ 53 | --root_path ./dataset/exchange_rate/ \ 54 | --data_path exchange_rate.csv \ 55 | --model_id Exchange_96_336 \ 56 | --model $model_name \ 57 | --data custom \ 58 | --features M \ 59 | --seq_len 720 \ 60 | --label_len 48 \ 61 | --pred_len 336 \ 62 | --e_layers 2 \ 63 | --d_layers 1 \ 64 | --factor 3 \ 65 | --enc_in 8 \ 66 | --dec_in 8 \ 67 | --c_out 8 \ 68 | --des 'Exp' \ 69 | --itr 1 70 | 71 | 72 | 73 | python -u run.py \ 74 | --task_name long_term_forecast \ 75 | --is_training 1 \ 76 | --root_path ./dataset/exchange_rate/ \ 77 | --data_path exchange_rate.csv \ 78 | --model_id Exchange_96_720 \ 79 | --model $model_name \ 80 | --data custom \ 81 | --features M \ 82 | --seq_len 720 \ 83 | --label_len 48 \ 84 | --pred_len 720 \ 85 | --e_layers 2 \ 86 | --d_layers 1 \ 87 | --factor 3 \ 88 | --enc_in 8 \ 89 | --dec_in 8 \ 90 | --c_out 8 \ 91 | --des 'Exp' \ 92 | --itr 1 93 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/Koopa.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_96_48 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 8 \ 20 | --dec_in 8 \ 21 | --c_out 8 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/exchange_rate/ \ 30 | --data_path exchange_rate.csv \ 31 | --model_id Exchange_192_96 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 8 \ 41 | --dec_in 8 \ 42 | --c_out 8 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/exchange_rate/ \ 51 | --data_path exchange_rate.csv \ 52 | --model_id Exchange_288_144 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 8 \ 62 | --dec_in 8 \ 63 | --c_out 8 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/exchange_rate/ \ 72 | --data_path exchange_rate.csv \ 73 | --model_id Exchange_384_192 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 8 \ 83 | --dec_in 8 \ 84 | --c_out 8 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/Mamba.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | for pred_len in 96 192 336 720 3 | do 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_$pred_len'_'$pred_len \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len $pred_len \ 15 | --label_len 48 \ 16 | --pred_len $pred_len \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --enc_in 8 \ 20 | --expand 2 \ 21 | --d_ff 16 \ 22 | --d_conv 4 \ 23 | --c_out 8 \ 24 | --d_model 128 \ 25 | --des 'Exp' \ 26 | --itr 1 \ 27 | 28 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/PatchTST.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 2 | 3 | model_name=PatchTST 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 8 \ 21 | --dec_in 8 \ 22 | --c_out 8 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/exchange_rate/ \ 30 | --data_path exchange_rate.csv \ 31 | --model_id Exchange_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 8 \ 42 | --dec_in 8 \ 43 | --c_out 8 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/exchange_rate/ \ 51 | --data_path exchange_rate.csv \ 52 | --model_id Exchange_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 8 \ 63 | --dec_in 8 \ 64 | --c_out 8 \ 65 | --des 'Exp' \ 66 | --itr 1 \ 67 | --train_epochs 1 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/exchange_rate/ \ 73 | --data_path exchange_rate.csv \ 74 | --model_id Exchange_96_720 \ 75 | --model $model_name \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 96 \ 79 | --label_len 48 \ 80 | --pred_len 720 \ 81 | --e_layers 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 8 \ 85 | --dec_in 8 \ 86 | --c_out 8 \ 87 | --des 'Exp' \ 88 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/Pyraformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 8 \ 21 | --dec_in 8 \ 22 | --c_out 8 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/exchange_rate/ \ 30 | --data_path exchange_rate.csv \ 31 | --model_id Exchange_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 8 \ 42 | --dec_in 8 \ 43 | --c_out 8 \ 44 | --des 'Exp' \ 45 | --itr 1 \ 46 | --train_epochs 1 47 | 48 | python -u run.py \ 49 | --task_name long_term_forecast \ 50 | --is_training 1 \ 51 | --root_path ./dataset/exchange_rate/ \ 52 | --data_path exchange_rate.csv \ 53 | --model_id Exchange_96_336 \ 54 | --model $model_name \ 55 | --data custom \ 56 | --features M \ 57 | --seq_len 96 \ 58 | --label_len 48 \ 59 | --pred_len 336 \ 60 | --e_layers 2 \ 61 | --d_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 8 \ 64 | --dec_in 8 \ 65 | --c_out 8 \ 66 | --des 'Exp' \ 67 | --itr 1 \ 68 | --train_epochs 1 69 | 70 | python -u run.py \ 71 | --task_name long_term_forecast \ 72 | --is_training 1 \ 73 | --root_path ./dataset/exchange_rate/ \ 74 | --data_path exchange_rate.csv \ 75 | --model_id Exchange_96_720 \ 76 | --model $model_name \ 77 | --data custom \ 78 | --features M \ 79 | --seq_len 96 \ 80 | --label_len 48 \ 81 | --pred_len 720 \ 82 | --e_layers 2 \ 83 | --d_layers 1 \ 84 | --factor 3 \ 85 | --enc_in 8 \ 86 | --dec_in 8 \ 87 | --c_out 8 \ 88 | --des 'Exp' \ 89 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Exchange_script/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/exchange_rate/ \ 9 | --data_path exchange_rate.csv \ 10 | --model_id Exchange_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 8 \ 21 | --dec_in 8 \ 22 | --c_out 8 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/exchange_rate/ \ 30 | --data_path exchange_rate.csv \ 31 | --model_id Exchange_96_192 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 96 \ 36 | --label_len 48 \ 37 | --pred_len 192 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 8 \ 42 | --dec_in 8 \ 43 | --c_out 8 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/exchange_rate/ \ 51 | --data_path exchange_rate.csv \ 52 | --model_id Exchange_96_336 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --label_len 48 \ 58 | --pred_len 336 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 8 \ 63 | --dec_in 8 \ 64 | --c_out 8 \ 65 | --des 'Exp' \ 66 | --itr 1 \ 67 | --train_epochs 1 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/exchange_rate/ \ 73 | --data_path exchange_rate.csv \ 74 | --model_id Exchange_96_720 \ 75 | --model $model_name \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 96 \ 79 | --label_len 48 \ 80 | --pred_len 720 \ 81 | --e_layers 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 8 \ 85 | --dec_in 8 \ 86 | --c_out 8 \ 87 | --des 'Exp' \ 88 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ILI_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/illness/ \ 9 | --data_path national_illness.csv \ 10 | --model_id ili_36_24 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 36 \ 15 | --label_len 18 \ 16 | --pred_len 24 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/illness/ \ 30 | --data_path national_illness.csv \ 31 | --model_id ili_36_36 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 36 \ 36 | --label_len 18 \ 37 | --pred_len 36 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/illness/ \ 51 | --data_path national_illness.csv \ 52 | --model_id ili_36_48 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 36 \ 57 | --label_len 18 \ 58 | --pred_len 48 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/illness/ \ 72 | --data_path national_illness.csv \ 73 | --model_id ili_36_60 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 36 \ 78 | --label_len 18 \ 79 | --pred_len 60 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ILI_script/FiLM.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 2 | 3 | python -u run.py \ 4 | --task_name long_term_forecast \ 5 | --is_training 1 \ 6 | --root_path ./dataset/illness/ \ 7 | --data_path national_illness.csv \ 8 | --model_id ili_36_24 \ 9 | --model FiLM \ 10 | --data custom \ 11 | --features M \ 12 | --seq_len 60 \ 13 | --label_len 18 \ 14 | --pred_len 24 \ 15 | --e_layers 2 \ 16 | --d_layers 1 \ 17 | --factor 3 \ 18 | --enc_in 7 \ 19 | --dec_in 7 \ 20 | --c_out 7 \ 21 | --des 'Exp' \ 22 | --itr 1 23 | 24 | 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/illness/ \ 30 | --data_path national_illness.csv \ 31 | --model_id ili_36_36 \ 32 | --model FiLM \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 60 \ 36 | --label_len 18 \ 37 | --pred_len 36 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/illness/ \ 51 | --data_path national_illness.csv \ 52 | --model_id ili_36_48 \ 53 | --model FiLM \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 60 \ 57 | --label_len 18 \ 58 | --pred_len 48 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/illness/ \ 73 | --data_path national_illness.csv \ 74 | --model_id ili_36_60 \ 75 | --model FiLM \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 60 \ 79 | --label_len 18 \ 80 | --pred_len 60 \ 81 | --e_layers 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 7 \ 85 | --dec_in 7 \ 86 | --c_out 7 \ 87 | --des 'Exp' \ 88 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ILI_script/Koopa.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/illness/ \ 9 | --data_path national_illness.csv \ 10 | --model_id ili_48_24 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 48 \ 15 | --pred_len 24 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 7 \ 20 | --dec_in 7 \ 21 | --c_out 7 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/illness/ \ 30 | --data_path national_illness.csv \ 31 | --model_id ili_72_36 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 72 \ 36 | --pred_len 36 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 7 \ 41 | --dec_in 7 \ 42 | --c_out 7 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/illness/ \ 51 | --data_path national_illness.csv \ 52 | --model_id ili_96_48 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 96 \ 57 | --pred_len 48 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 7 \ 62 | --dec_in 7 \ 63 | --c_out 7 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/illness/ \ 72 | --data_path national_illness.csv \ 73 | --model_id ili_120_60 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 120 \ 78 | --pred_len 60 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 7 \ 83 | --dec_in 7 \ 84 | --c_out 7 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/ILI_script/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/illness/ \ 9 | --data_path national_illness.csv \ 10 | --model_id ili_36_24 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 36 \ 15 | --label_len 18 \ 16 | --pred_len 24 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/illness/ \ 30 | --data_path national_illness.csv \ 31 | --model_id ili_36_36 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 36 \ 36 | --label_len 18 \ 37 | --pred_len 36 \ 38 | --e_layers 2 \ 39 | --d_layers 1 \ 40 | --factor 3 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/illness/ \ 51 | --data_path national_illness.csv \ 52 | --model_id ili_36_48 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 36 \ 57 | --label_len 18 \ 58 | --pred_len 48 \ 59 | --e_layers 2 \ 60 | --d_layers 1 \ 61 | --factor 3 \ 62 | --enc_in 7 \ 63 | --dec_in 7 \ 64 | --c_out 7 \ 65 | --des 'Exp' \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/illness/ \ 72 | --data_path national_illness.csv \ 73 | --model_id ili_36_60 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 36 \ 78 | --label_len 18 \ 79 | --pred_len 60 \ 80 | --e_layers 2 \ 81 | --d_layers 1 \ 82 | --factor 3 \ 83 | --enc_in 7 \ 84 | --dec_in 7 \ 85 | --c_out 7 \ 86 | --des 'Exp' \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Mamba_all.sh: -------------------------------------------------------------------------------- 1 | ./scripts/long_term_forecast/ECL_script/Mamba.sh 2 | ./scripts/long_term_forecast/Traffic_script/Mamba.sh 3 | ./scripts/long_term_forecast/Exchange_script/Mamba.sh 4 | ./scripts/long_term_forecast/Weather_script/Mamba.sh 5 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/traffic/ \ 9 | --data_path traffic.csv \ 10 | --model_id traffic_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 862 \ 21 | --dec_in 862 \ 22 | --c_out 862 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --train_epochs 3 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/traffic/ \ 31 | --data_path traffic.csv \ 32 | --model_id traffic_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 862 \ 43 | --dec_in 862 \ 44 | --c_out 862 \ 45 | --des 'Exp' \ 46 | --itr 1 \ 47 | --train_epochs 3 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/traffic/ \ 53 | --data_path traffic.csv \ 54 | --model_id traffic_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 862 \ 65 | --dec_in 862 \ 66 | --c_out 862 \ 67 | --des 'Exp' \ 68 | --itr 1 \ 69 | --train_epochs 3 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/traffic/ \ 75 | --data_path traffic.csv \ 76 | --model_id traffic_96_720 \ 77 | --model $model_name \ 78 | --data custom \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 862 \ 87 | --dec_in 862 \ 88 | --c_out 862 \ 89 | --des 'Exp' \ 90 | --itr 1 \ 91 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/FiLM.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=3 2 | 3 | model_name=FiLM 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/traffic/ \ 9 | --data_path traffic.csv \ 10 | --model_id traffic_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 720 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 862 \ 21 | --dec_in 862 \ 22 | --c_out 862 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --batch_size 2 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/traffic/ \ 31 | --data_path traffic.csv \ 32 | --model_id traffic_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 720 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 862 \ 43 | --dec_in 862 \ 44 | --c_out 862 \ 45 | --des 'Exp' \ 46 | --itr 1 \ 47 | --batch_size 2 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/traffic/ \ 53 | --data_path traffic.csv \ 54 | --model_id traffic_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features M \ 58 | --seq_len 720 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 862 \ 65 | --dec_in 862 \ 66 | --c_out 862 \ 67 | --des 'Exp' \ 68 | --itr 1 \ 69 | --batch_size 2 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/traffic/ \ 75 | --data_path traffic.csv \ 76 | --model_id traffic_96_720 \ 77 | --model $model_name \ 78 | --data custom \ 79 | --features M \ 80 | --seq_len 720 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 862 \ 87 | --dec_in 862 \ 88 | --c_out 862 \ 89 | --des 'Exp' \ 90 | --itr 1 \ 91 | --batch_size 2 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/Koopa.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=Koopa 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/traffic/ \ 9 | --data_path traffic.csv \ 10 | --model_id traffic_96_48 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --pred_len 48 \ 16 | --e_layers 2 \ 17 | --d_layers 1 \ 18 | --factor 3 \ 19 | --enc_in 862 \ 20 | --dec_in 862 \ 21 | --c_out 862 \ 22 | --des 'Exp' \ 23 | --learning_rate 0.001 \ 24 | --itr 1 25 | 26 | python -u run.py \ 27 | --task_name long_term_forecast \ 28 | --is_training 1 \ 29 | --root_path ./dataset/traffic/ \ 30 | --data_path traffic.csv \ 31 | --model_id traffic_192_96 \ 32 | --model $model_name \ 33 | --data custom \ 34 | --features M \ 35 | --seq_len 192 \ 36 | --pred_len 96 \ 37 | --e_layers 2 \ 38 | --d_layers 1 \ 39 | --factor 3 \ 40 | --enc_in 862 \ 41 | --dec_in 862 \ 42 | --c_out 862 \ 43 | --des 'Exp' \ 44 | --learning_rate 0.001 \ 45 | --itr 1 46 | 47 | python -u run.py \ 48 | --task_name long_term_forecast \ 49 | --is_training 1 \ 50 | --root_path ./dataset/traffic/ \ 51 | --data_path traffic.csv \ 52 | --model_id traffic_288_144 \ 53 | --model $model_name \ 54 | --data custom \ 55 | --features M \ 56 | --seq_len 288 \ 57 | --pred_len 144 \ 58 | --e_layers 2 \ 59 | --d_layers 1 \ 60 | --factor 3 \ 61 | --enc_in 862 \ 62 | --dec_in 862 \ 63 | --c_out 862 \ 64 | --des 'Exp' \ 65 | --learning_rate 0.001 \ 66 | --itr 1 67 | 68 | python -u run.py \ 69 | --task_name long_term_forecast \ 70 | --is_training 1 \ 71 | --root_path ./dataset/traffic/ \ 72 | --data_path traffic.csv \ 73 | --model_id traffic_384_192 \ 74 | --model $model_name \ 75 | --data custom \ 76 | --features M \ 77 | --seq_len 384 \ 78 | --pred_len 192 \ 79 | --e_layers 2 \ 80 | --d_layers 1 \ 81 | --factor 3 \ 82 | --enc_in 862 \ 83 | --dec_in 862 \ 84 | --c_out 862 \ 85 | --des 'Exp' \ 86 | --learning_rate 0.001 \ 87 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/Mamba.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | 3 | for pred_len in 96 192 336 720 4 | do 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/traffic/ \ 10 | --data_path traffic.csv \ 11 | --model_id traffic_$pred_len'_'$pred_len \ 12 | --model $model_name \ 13 | --data custom \ 14 | --features M \ 15 | --seq_len $pred_len \ 16 | --label_len 48 \ 17 | --pred_len $pred_len \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --enc_in 862 \ 21 | --expand 2 \ 22 | --d_ff 16 \ 23 | --d_conv 4 \ 24 | --c_out 862 \ 25 | --d_model 128 \ 26 | --des 'Exp' \ 27 | --itr 1 \ 28 | 29 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/Pyraformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/traffic/ \ 9 | --data_path traffic.csv \ 10 | --model_id traffic_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 862 \ 21 | --dec_in 862 \ 22 | --c_out 862 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --train_epochs 3 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/traffic/ \ 31 | --data_path traffic.csv \ 32 | --model_id traffic_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 862 \ 43 | --dec_in 862 \ 44 | --c_out 862 \ 45 | --des 'Exp' \ 46 | --itr 1 \ 47 | --train_epochs 3 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/traffic/ \ 53 | --data_path traffic.csv \ 54 | --model_id traffic_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 862 \ 65 | --dec_in 862 \ 66 | --c_out 862 \ 67 | --des 'Exp' \ 68 | --itr 1 \ 69 | --train_epochs 3 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/traffic/ \ 75 | --data_path traffic.csv \ 76 | --model_id traffic_96_720 \ 77 | --model $model_name \ 78 | --data custom \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 862 \ 87 | --dec_in 862 \ 88 | --c_out 862 \ 89 | --des 'Exp' \ 90 | --itr 1 \ 91 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/SegRNN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/traffic/ \ 12 | --data_path traffic.csv \ 13 | --model_id traffic_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data custom \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 24 \ 20 | --enc_in 862 \ 21 | --d_model 512 \ 22 | --dropout 0 \ 23 | --learning_rate 0.001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | 28 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/traffic/ \ 9 | --data_path traffic.csv \ 10 | --model_id traffic_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 862 \ 21 | --dec_in 862 \ 22 | --c_out 862 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --train_epochs 3 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/traffic/ \ 31 | --data_path traffic.csv \ 32 | --model_id traffic_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 862 \ 43 | --dec_in 862 \ 44 | --c_out 862 \ 45 | --des 'Exp' \ 46 | --itr 1 \ 47 | --train_epochs 3 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/traffic/ \ 53 | --data_path traffic.csv \ 54 | --model_id traffic_96_336 \ 55 | --model $model_name \ 56 | --data custom \ 57 | --features M \ 58 | --seq_len 96 \ 59 | --label_len 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --factor 3 \ 64 | --enc_in 862 \ 65 | --dec_in 862 \ 66 | --c_out 862 \ 67 | --des 'Exp' \ 68 | --itr 1 \ 69 | --train_epochs 3 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/traffic/ \ 75 | --data_path traffic.csv \ 76 | --model_id traffic_96_720 \ 77 | --model $model_name \ 78 | --data custom \ 79 | --features M \ 80 | --seq_len 96 \ 81 | --label_len 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --factor 3 \ 86 | --enc_in 862 \ 87 | --dec_in 862 \ 88 | --c_out 862 \ 89 | --des 'Exp' \ 90 | --itr 1 \ 91 | --train_epochs 3 92 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Traffic_script/WPMixer.sh: -------------------------------------------------------------------------------- 1 | 2 | export CUDA_VISIBLE_DEVICES=0 3 | 4 | # Model name 5 | model_name=WPMixer 6 | 7 | # Datasets and prediction lengths 8 | dataset=traffic 9 | seq_lens=(1200 1200 1200 1200) 10 | pred_lens=(96 192 336 720) 11 | learning_rates=(0.0010385 0.000567053 0.001026715 0.001496217) 12 | batches=(16 16 16 16) 13 | epochs=(60 60 50 60) 14 | dropouts=(0.05 0.05 0.0 0.05) 15 | patch_lens=(16 16 16 16) 16 | lradjs=(type3 type3 type3 type3) 17 | d_models=(16 32 32 32) 18 | patiences=(12 12 12 12) 19 | 20 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 21 | wavelets=(db3 db3 bior3.1 db3) 22 | levels=(1 1 1 1) 23 | tfactors=(3 3 7 7) 24 | dfactors=(5 5 7 3) 25 | strides=(8 8 8 8) 26 | 27 | # Loop over datasets and prediction lengths 28 | for i in "${!pred_lens[@]}"; do 29 | python -u run.py \ 30 | --is_training 1 \ 31 | --root_path ./data/traffic/ \ 32 | --data_path traffic.csv \ 33 | --model_id wpmixer \ 34 | --model $model_name \ 35 | --task_name long_term_forecast \ 36 | --data $dataset \ 37 | --seq_len ${seq_lens[$i]} \ 38 | --pred_len ${pred_lens[$i]} \ 39 | --label_len 0 \ 40 | --d_model ${d_models[$i]} \ 41 | --patch_len ${patch_lens[$i]} \ 42 | --batch_size ${batches[$i]} \ 43 | --learning_rate ${learning_rates[$i]} \ 44 | --lradj ${lradjs[$i]} \ 45 | --dropout ${dropouts[$i]} \ 46 | --patience ${patiences[$i]} \ 47 | --train_epochs ${epochs[$i]} \ 48 | --use_amp 49 | done 50 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=Autoformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/weather/ \ 9 | --data_path weather.csv \ 10 | --model_id weather_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 21 \ 21 | --dec_in 21 \ 22 | --c_out 21 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --train_epochs 2 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/weather/ \ 31 | --data_path weather.csv \ 32 | --model_id weather_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 21 \ 43 | --dec_in 21 \ 44 | --c_out 21 \ 45 | --des 'Exp' \ 46 | --itr 1 47 | 48 | python -u run.py \ 49 | --task_name long_term_forecast \ 50 | --is_training 1 \ 51 | --root_path ./dataset/weather/ \ 52 | --data_path weather.csv \ 53 | --model_id weather_96_336 \ 54 | --model $model_name \ 55 | --data custom \ 56 | --features M \ 57 | --seq_len 96 \ 58 | --label_len 48 \ 59 | --pred_len 336 \ 60 | --e_layers 2 \ 61 | --d_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 21 \ 64 | --dec_in 21 \ 65 | --c_out 21 \ 66 | --des 'Exp' \ 67 | --itr 1 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/weather/ \ 73 | --data_path weather.csv \ 74 | --model_id weather_96_720 \ 75 | --model $model_name \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 96 \ 79 | --label_len 48 \ 80 | --pred_len 720 \ 81 | --e_layers 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 21 \ 85 | --dec_in 21 \ 86 | --c_out 21 \ 87 | --des 'Exp' \ 88 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/FiLM.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | python -u run.py \ 4 | --task_name long_term_forecast \ 5 | --is_training 1 \ 6 | --root_path ./dataset/weather/ \ 7 | --data_path weather.csv \ 8 | --model_id weather_96_720 \ 9 | --model FiLM \ 10 | --data custom \ 11 | --features M \ 12 | --seq_len 720 \ 13 | --label_len 48 \ 14 | --pred_len 720 \ 15 | --e_layers 2 \ 16 | --d_layers 1 \ 17 | --factor 3 \ 18 | --enc_in 21 \ 19 | --dec_in 21 \ 20 | --c_out 21 \ 21 | --des 'Exp' \ 22 | --itr 1 23 | 24 | python -u run.py \ 25 | --task_name long_term_forecast \ 26 | --is_training 1 \ 27 | --root_path ./dataset/weather/ \ 28 | --data_path weather.csv \ 29 | --model_id weather_96_96 \ 30 | --model FiLM \ 31 | --data custom \ 32 | --features M \ 33 | --seq_len 96 \ 34 | --label_len 48 \ 35 | --pred_len 96 \ 36 | --e_layers 2 \ 37 | --d_layers 1 \ 38 | --factor 3 \ 39 | --enc_in 21 \ 40 | --dec_in 21 \ 41 | --c_out 21 \ 42 | --des 'Exp' \ 43 | --itr 1 44 | 45 | python -u run.py \ 46 | --task_name long_term_forecast \ 47 | --is_training 1 \ 48 | --root_path ./dataset/weather/ \ 49 | --data_path weather.csv \ 50 | --model_id weather_96_192 \ 51 | --model FiLM \ 52 | --data custom \ 53 | --features M \ 54 | --seq_len 192 \ 55 | --label_len 48 \ 56 | --pred_len 192 \ 57 | --e_layers 2 \ 58 | --d_layers 1 \ 59 | --factor 3 \ 60 | --enc_in 21 \ 61 | --dec_in 21 \ 62 | --c_out 21 \ 63 | --des 'Exp' \ 64 | --itr 1 65 | 66 | python -u run.py \ 67 | --task_name long_term_forecast \ 68 | --is_training 1 \ 69 | --root_path ./dataset/weather/ \ 70 | --data_path weather.csv \ 71 | --model_id weather_96_336 \ 72 | --model FiLM \ 73 | --data custom \ 74 | --features M \ 75 | --seq_len 336 \ 76 | --label_len 48 \ 77 | --pred_len 336 \ 78 | --e_layers 2 \ 79 | --d_layers 1 \ 80 | --factor 3 \ 81 | --enc_in 21 \ 82 | --dec_in 21 \ 83 | --c_out 21 \ 84 | --des 'Exp' \ 85 | --itr 1 86 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/Mamba.sh: -------------------------------------------------------------------------------- 1 | model_name=Mamba 2 | 3 | for pred_len in 96 192 336 720 4 | do 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/weather/ \ 10 | --data_path weather.csv \ 11 | --model_id weather_$pred_len'_'$pred_len \ 12 | --model $model_name \ 13 | --data custom \ 14 | --features M \ 15 | --seq_len $pred_len \ 16 | --label_len 48 \ 17 | --pred_len $pred_len \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --enc_in 21 \ 21 | --expand 2 \ 22 | --d_ff 16 \ 23 | --d_conv 4 \ 24 | --c_out 21 \ 25 | --d_model 128 \ 26 | --des 'Exp' \ 27 | --itr 1 \ 28 | 29 | done -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/Pyraformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | model_name=Pyraformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/weather/ \ 9 | --data_path weather.csv \ 10 | --model_id weather_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 21 \ 21 | --dec_in 21 \ 22 | --c_out 21 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --train_epochs 2 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/weather/ \ 31 | --data_path weather.csv \ 32 | --model_id weather_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 21 \ 43 | --dec_in 21 \ 44 | --c_out 21 \ 45 | --des 'Exp' \ 46 | --itr 1 47 | 48 | python -u run.py \ 49 | --task_name long_term_forecast \ 50 | --is_training 1 \ 51 | --root_path ./dataset/weather/ \ 52 | --data_path weather.csv \ 53 | --model_id weather_96_336 \ 54 | --model $model_name \ 55 | --data custom \ 56 | --features M \ 57 | --seq_len 96 \ 58 | --label_len 48 \ 59 | --pred_len 336 \ 60 | --e_layers 2 \ 61 | --d_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 21 \ 64 | --dec_in 21 \ 65 | --c_out 21 \ 66 | --des 'Exp' \ 67 | --itr 1 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/weather/ \ 73 | --data_path weather.csv \ 74 | --model_id weather_96_720 \ 75 | --model $model_name \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 96 \ 79 | --label_len 48 \ 80 | --pred_len 720 \ 81 | --e_layers 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 21 \ 85 | --dec_in 21 \ 86 | --c_out 21 \ 87 | --des 'Exp' \ 88 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/SegRNN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=SegRNN 4 | 5 | seq_len=96 6 | for pred_len in 96 192 336 720 7 | do 8 | python -u run.py \ 9 | --task_name long_term_forecast \ 10 | --is_training 1 \ 11 | --root_path ./dataset/weather/ \ 12 | --data_path weather.csv \ 13 | --model_id weather_$seq_len'_'$pred_len \ 14 | --model $model_name \ 15 | --data custom \ 16 | --features M \ 17 | --seq_len $seq_len \ 18 | --pred_len $pred_len \ 19 | --seg_len 48 \ 20 | --enc_in 21 \ 21 | --d_model 512 \ 22 | --dropout 0.5 \ 23 | --learning_rate 0.0001 \ 24 | --des 'Exp' \ 25 | --itr 1 26 | done 27 | 28 | -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | model_name=Transformer 4 | 5 | python -u run.py \ 6 | --task_name long_term_forecast \ 7 | --is_training 1 \ 8 | --root_path ./dataset/weather/ \ 9 | --data_path weather.csv \ 10 | --model_id weather_96_96 \ 11 | --model $model_name \ 12 | --data custom \ 13 | --features M \ 14 | --seq_len 96 \ 15 | --label_len 48 \ 16 | --pred_len 96 \ 17 | --e_layers 2 \ 18 | --d_layers 1 \ 19 | --factor 3 \ 20 | --enc_in 21 \ 21 | --dec_in 21 \ 22 | --c_out 21 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --train_epochs 3 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/weather/ \ 31 | --data_path weather.csv \ 32 | --model_id weather_96_192 \ 33 | --model $model_name \ 34 | --data custom \ 35 | --features M \ 36 | --seq_len 96 \ 37 | --label_len 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --factor 3 \ 42 | --enc_in 21 \ 43 | --dec_in 21 \ 44 | --c_out 21 \ 45 | --des 'Exp' \ 46 | --itr 1 47 | 48 | python -u run.py \ 49 | --task_name long_term_forecast \ 50 | --is_training 1 \ 51 | --root_path ./dataset/weather/ \ 52 | --data_path weather.csv \ 53 | --model_id weather_96_336 \ 54 | --model $model_name \ 55 | --data custom \ 56 | --features M \ 57 | --seq_len 96 \ 58 | --label_len 48 \ 59 | --pred_len 336 \ 60 | --e_layers 2 \ 61 | --d_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 21 \ 64 | --dec_in 21 \ 65 | --c_out 21 \ 66 | --des 'Exp' \ 67 | --itr 1 68 | 69 | python -u run.py \ 70 | --task_name long_term_forecast \ 71 | --is_training 1 \ 72 | --root_path ./dataset/weather/ \ 73 | --data_path weather.csv \ 74 | --model_id weather_96_720 \ 75 | --model $model_name \ 76 | --data custom \ 77 | --features M \ 78 | --seq_len 96 \ 79 | --label_len 48 \ 80 | --pred_len 720 \ 81 | --e_layers 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 21 \ 85 | --dec_in 21 \ 86 | --c_out 21 \ 87 | --des 'Exp' \ 88 | --itr 1 -------------------------------------------------------------------------------- /scripts/long_term_forecast/Weather_script/WPMixer.sh: -------------------------------------------------------------------------------- 1 | 2 | export CUDA_VISIBLE_DEVICES=0 3 | 4 | # Model name 5 | model_name=WPMixer 6 | 7 | # Datasets and prediction lengths 8 | dataset=weather 9 | seq_lens=(512 512 512 512) 10 | pred_lens=(96 192 336 720) 11 | learning_rates=(0.000913333 0.001379042 0.000607991 0.001470479) 12 | batches=(32 64 32 128) 13 | epochs=(60 60 60 60) 14 | dropouts=(0.4 0.4 0.4 0.4) 15 | patch_lens=(16 16 16 16) 16 | lradjs=(type3 type3 type3 type3) 17 | d_models=(256 128 128 128) 18 | patiences=(12 12 12 12) 19 | 20 | # Model params below need to be set in WPMixer.py Line 15, instead of this script 21 | wavelets=(db3 db3 db3 db2) 22 | levels=(2 1 2 1) 23 | tfactors=(3 3 7 7) 24 | dfactors=(7 7 7 5) 25 | strides=(8 8 8 8) 26 | 27 | # Loop over datasets and prediction lengths 28 | for i in "${!pred_lens[@]}"; do 29 | python -u run.py \ 30 | --is_training 1 \ 31 | --root_path ./data/weather/ \ 32 | --data_path weather.csv \ 33 | --model_id wpmixer \ 34 | --model $model_name \ 35 | --task_name long_term_forecast \ 36 | --data $dataset \ 37 | --seq_len ${seq_lens[$i]} \ 38 | --pred_len ${pred_lens[$i]} \ 39 | --label_len 0 \ 40 | --d_model ${d_models[$i]} \ 41 | --patch_len ${patch_lens[$i]} \ 42 | --batch_size ${batches[$i]} \ 43 | --learning_rate ${learning_rates[$i]} \ 44 | --lradj ${lradjs[$i]} \ 45 | --dropout ${dropouts[$i]} \ 46 | --patience ${patiences[$i]} \ 47 | --train_epochs ${epochs[$i]} \ 48 | --use_amp 49 | done 50 | -------------------------------------------------------------------------------- /tutorial/conv.png: 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https://raw.githubusercontent.com/thuml/Time-Series-Library/85c08390b6ecc5a5c3bae33b3880b8bc3e413023/tutorial/result.png -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Time-Series-Library/85c08390b6ecc5a5c3bae33b3880b8bc3e413023/utils/__init__.py -------------------------------------------------------------------------------- /utils/masking.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class TriangularCausalMask(): 5 | def __init__(self, B, L, device="cpu"): 6 | mask_shape = [B, 1, L, L] 7 | with torch.no_grad(): 8 | self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device) 9 | 10 | @property 11 | def mask(self): 12 | return self._mask 13 | 14 | 15 | class ProbMask(): 16 | def __init__(self, B, H, L, index, scores, device="cpu"): 17 | _mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).to(device).triu(1) 18 | _mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1]) 19 | indicator = _mask_ex[torch.arange(B)[:, None, None], 20 | torch.arange(H)[None, :, None], 21 | index, :].to(device) 22 | self._mask = indicator.view(scores.shape).to(device) 23 | 24 | @property 25 | def mask(self): 26 | return self._mask 27 | -------------------------------------------------------------------------------- /utils/metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def RSE(pred, true): 5 | return np.sqrt(np.sum((true - pred) ** 2)) / np.sqrt(np.sum((true - true.mean()) ** 2)) 6 | 7 | 8 | def CORR(pred, true): 9 | u = ((true - true.mean(0)) * (pred - pred.mean(0))).sum(0) 10 | d = np.sqrt(((true - true.mean(0)) ** 2 * (pred - pred.mean(0)) ** 2).sum(0)) 11 | return (u / d).mean(-1) 12 | 13 | 14 | def MAE(pred, true): 15 | return np.mean(np.abs(true - pred)) 16 | 17 | 18 | def MSE(pred, true): 19 | return np.mean((true - pred) ** 2) 20 | 21 | 22 | def RMSE(pred, true): 23 | return np.sqrt(MSE(pred, true)) 24 | 25 | 26 | def MAPE(pred, true): 27 | return np.mean(np.abs((true - pred) / true)) 28 | 29 | 30 | def MSPE(pred, true): 31 | return np.mean(np.square((true - pred) / true)) 32 | 33 | 34 | def metric(pred, true): 35 | mae = MAE(pred, true) 36 | mse = MSE(pred, true) 37 | rmse = RMSE(pred, true) 38 | mape = MAPE(pred, true) 39 | mspe = MSPE(pred, true) 40 | 41 | return mae, mse, rmse, mape, mspe 42 | --------------------------------------------------------------------------------