├── .gitignore ├── LICENSE ├── README.md ├── checkpoints └── long_term_forecast_ECL_96_96_Client_custom_ftM_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0 │ └── checkpoint.pth ├── data_provider ├── __init__.py ├── data_factory.py ├── data_loader.py ├── m4.py └── uea.py ├── dataset ├── 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 ├── ETSformer_EncDec.py ├── Embed.py ├── FourierCorrelation.py ├── MultiWaveletCorrelation.py ├── Pyraformer_EncDec.py ├── RevIN.py ├── SelfAttention_Family.py ├── Transformer_EncDec.py └── __init__.py ├── mask_result.csv ├── models ├── Autoformer.py ├── Client.py ├── Crossformer.py ├── DLinear.py ├── ETSformer.py ├── FEDformer.py ├── Informer.py ├── LightTS.py ├── Linear.py ├── MICN.py ├── Nonstationary_Transformer.py ├── PatchTST.py ├── Pyraformer.py ├── Reformer.py ├── TimesNet.py ├── Transformer.py └── __init__.py ├── requirements.txt ├── result_of_Client.txt ├── run.py ├── scripts ├── ECL_script │ ├── Autoformer.sh │ ├── Client.sh │ ├── Crossformer.sh │ ├── DLinear.sh │ ├── ETSformer.sh │ ├── FEDformer.sh │ ├── Informer.sh │ ├── LightTS.sh │ ├── Linear.sh │ ├── MICN.sh │ ├── Nonstationary_Transformer.sh │ ├── PatchTST.sh │ ├── Pyraformer.sh │ ├── Reformer.sh │ ├── TimesNet.sh │ └── Transformer.sh ├── ETT_script │ ├── Autoformer_ETTh1.sh │ ├── Autoformer_ETTh2.sh │ ├── Autoformer_ETTm1.sh │ ├── Autoformer_ETTm2.sh │ ├── Client_ETTh1.sh │ ├── Client_ETTh2.sh │ ├── Client_ETTm1.sh │ ├── Client_ETTm2.sh │ ├── Crossformer_ETTh1.sh │ ├── Crossformer_ETTh2.sh │ ├── Crossformer_ETTm1.sh │ ├── Crossformer_ETTm2.sh │ ├── DLinear_ETTh1.sh │ ├── ETSformer_ETTh1.sh │ ├── FEDformer_ETTh1.sh │ ├── Informer_ETTh1.sh │ ├── LightTS_ETTh1.sh │ ├── MICN_ETTh1.sh │ ├── MICN_ETTh2.sh │ ├── MICN_ETTm1.sh │ ├── MICN_ETTm2.sh │ ├── Nonstationary_Transformer_ETTh1.sh │ ├── Nonstationary_Transformer_ETTh2.sh │ ├── Nonstationary_Transformer_ETTm1.sh │ ├── Nonstationary_Transformer_ETTm2.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 │ ├── TimesNet_ETTh1.sh │ ├── TimesNet_ETTh2.sh │ ├── TimesNet_ETTm1.sh │ ├── TimesNet_ETTm2.sh │ ├── Transformer_ETTh1.sh │ ├── Transformer_ETTh2.sh │ ├── Transformer_ETTm1.sh │ └── Transformer_ETTm2.sh ├── Exchange_script │ ├── Autoformer.sh │ ├── Client.sh │ ├── Crossformer.sh │ ├── MICN.sh │ ├── Nonstationary_Transformer.sh │ ├── PatchTST.sh │ ├── Pyraformer.sh │ ├── TimesNet.sh │ └── Transformer.sh ├── ILI_script │ ├── Autoformer.sh │ ├── Client.sh │ ├── Crossformer.sh │ ├── MICN.sh │ ├── Nonstationary_Transformer.sh │ ├── PatchTST.sh │ ├── TimesNet.sh │ └── Transformer.sh ├── Traffic_script │ ├── Autoformer.sh │ ├── Client.sh │ ├── Crossformer.sh │ ├── MICN.sh │ ├── Nonstationary_Transformer.sh │ ├── PatchTST.sh │ ├── Pyraformer.sh │ ├── TimesNet.sh │ └── Transformer.sh └── Weather_script │ ├── Autoformer.sh │ ├── Client.sh │ ├── Crossformer.sh │ ├── MICN.sh │ ├── Nonstationary_Transformer.sh │ ├── PatchTST.sh │ ├── Pyraformer.sh │ ├── TimesNet.sh │ └── Transformer.sh ├── utils ├── __init__.py ├── losses.py ├── m4_summary.py ├── masking.py ├── metrics.py ├── timefeatures.py └── tools.py └── visualization.ipynb /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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting (Client) 2 | 3 | This is the official repo for Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting (Client). 4 | 5 | ## Getting Started 6 | 7 | 1. Install Python >= 3.6, and install the dependencies by: 8 | 9 | ``` 10 | pip install -r requirements.txt 11 | ``` 12 | 13 | 2. You can obtain all the nine datasets from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing), [[Tsinghua Cloud]](https://cloud.tsinghua.edu.cn/f/84fbc752d0e94980a610/) or [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy) provided in TimesNet and put them into the folder `./dataset`. 14 | 15 | 3. You can reproduce the experiment results through through the training scripts `./scripts/`, and the name of our model's scripts is started with 'Client'. 16 | 17 | ``` 18 | # ETTh1 19 | bash ./scripts/ETT_script/Client_ETTh1.sh 20 | # ECL 21 | bash ./scripts/ECL_script/Client.sh 22 | ``` 23 | 24 | 4. You can visualize the predictions of Client through the notebook 'visualization.ipynb'. 25 | 26 | 5. The origin experimental results of mask series are shown in 'mask_result.csv', and the origin experimental results of LTSF are shown in 'result_of_Client.txt'. 27 | 28 | ## Citation 29 | 30 | If you find our repo useful, please cite our paper: 31 | 32 | ``` 33 | @misc{gao2023client, 34 | title={Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting}, 35 | author={Jiaxin Gao and Wenbo Hu and Yuntian Chen}, 36 | year={2023}, 37 | eprint={2305.18838}, 38 | archivePrefix={arXiv}, 39 | primaryClass={cs.LG} 40 | } 41 | ``` 42 | 43 | ## Acknowledgement 44 | 45 | We appreciate the following repos for their valuable code base or datasets: 46 | 47 | https://github.com/thuml/Time-Series-Library 48 | 49 | https://github.com/cure-lab/LTSF-Linear 50 | -------------------------------------------------------------------------------- /checkpoints/long_term_forecast_ECL_96_96_Client_custom_ftM_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/checkpoint.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daxin007/Client/802c603f5ff3dd7814afd3e096f9082873373f1e/checkpoints/long_term_forecast_ECL_96_96_Client_custom_ftM_sl96_ll48_pl96_dm512_nh8_el2_dl1_df2048_fc3_ebtimeF_dtTrue_Exp_0/checkpoint.pth -------------------------------------------------------------------------------- /data_provider/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /dataset: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daxin007/Client/802c603f5ff3dd7814afd3e096f9082873373f1e/dataset -------------------------------------------------------------------------------- /exp/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daxin007/Client/802c603f5ff3dd7814afd3e096f9082873373f1e/exp/__init__.py -------------------------------------------------------------------------------- /exp/exp_basic.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | from models import Autoformer, Transformer, TimesNet, Nonstationary_Transformer, DLinear, FEDformer, \ 4 | Informer, LightTS, Reformer, ETSformer, Pyraformer, PatchTST, MICN, Crossformer, Client, Linear 5 | 6 | 7 | class Exp_Basic(object): 8 | def __init__(self, args): 9 | self.args = args 10 | self.model_dict = { 11 | 'TimesNet': TimesNet, 12 | 'Autoformer': Autoformer, 13 | 'Transformer': Transformer, 14 | 'Nonstationary_Transformer': Nonstationary_Transformer, 15 | 'DLinear': DLinear, 16 | 'FEDformer': FEDformer, 17 | 'Informer': Informer, 18 | 'LightTS': LightTS, 19 | 'Reformer': Reformer, 20 | 'ETSformer': ETSformer, 21 | 'PatchTST': PatchTST, 22 | 'Pyraformer': Pyraformer, 23 | 'MICN': MICN, 24 | 'Crossformer': Crossformer, 25 | 'Client': Client, 26 | 'Linear':Linear, 27 | } 28 | self.device = self._acquire_device() 29 | self.model = self._build_model().to(self.device) 30 | 31 | def _build_model(self): 32 | raise NotImplementedError 33 | return None 34 | 35 | def _acquire_device(self): 36 | if self.args.use_gpu: 37 | os.environ["CUDA_VISIBLE_DEVICES"] = str( 38 | self.args.gpu) if not self.args.use_multi_gpu else self.args.devices 39 | device = torch.device('cuda:{}'.format(self.args.gpu)) 40 | print('Use GPU: cuda:{}'.format(self.args.gpu)) 41 | else: 42 | device = torch.device('cpu') 43 | print('Use CPU') 44 | return device 45 | 46 | def _get_data(self): 47 | pass 48 | 49 | def vali(self): 50 | pass 51 | 52 | def train(self): 53 | pass 54 | 55 | def test(self): 56 | pass 57 | -------------------------------------------------------------------------------- /layers/RevIN.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | class RevIN(nn.Module): 5 | def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False): 6 | """ 7 | :param num_features: the number of features or channels 8 | :param eps: a value added for numerical stability 9 | :param affine: if True, RevIN has learnable affine parameters 10 | """ 11 | super(RevIN, self).__init__() 12 | self.num_features = num_features 13 | self.eps = eps 14 | self.affine = affine 15 | self.subtract_last = subtract_last 16 | if self.affine: 17 | self._init_params() 18 | 19 | def forward(self, x, mode:str): 20 | if mode == 'norm': 21 | self._get_statistics(x) 22 | x = self._normalize(x) 23 | elif mode == 'denorm': 24 | x = self._denormalize(x) 25 | else: raise NotImplementedError 26 | return x 27 | 28 | def _init_params(self): 29 | # initialize RevIN params: (C,) 30 | self.affine_weight = nn.Parameter(torch.ones(self.num_features)) 31 | self.affine_bias = nn.Parameter(torch.zeros(self.num_features)) 32 | 33 | def _get_statistics(self, x): 34 | dim2reduce = tuple(range(1, x.ndim-1)) 35 | if self.subtract_last: 36 | self.last = x[:,-1,:].unsqueeze(1) 37 | else: 38 | self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach() 39 | self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach() 40 | 41 | def _normalize(self, x): 42 | if self.subtract_last: 43 | x = x - self.last 44 | else: 45 | x = x - self.mean 46 | x = x / self.stdev 47 | if self.affine: 48 | x = x * self.affine_weight 49 | x = x + self.affine_bias 50 | return x 51 | 52 | def _denormalize(self, x): 53 | if self.affine: 54 | x = x - self.affine_bias 55 | x = x / (self.affine_weight + self.eps*self.eps) 56 | x = x * self.stdev 57 | if self.subtract_last: 58 | x = x + self.last 59 | else: 60 | x = x + self.mean 61 | return x 62 | -------------------------------------------------------------------------------- /layers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daxin007/Client/802c603f5ff3dd7814afd3e096f9082873373f1e/layers/__init__.py -------------------------------------------------------------------------------- /mask_result.csv: -------------------------------------------------------------------------------- 1 | ,mse,mae,10% mse,10% mae,30% mse,30% mae,50% mse,50% mae,80% mse,80% mae 2 | autoformer,0.248,0.352,0.238,0.345,0.248,0.351,0.255,0.354,0.288,0.386 3 | informer,0.358,0.435,0.351,0.43,,,,,0.32,0.405 4 | fedformer,0.223,0.334,0.225,0.335,0.22,0.333,0.224,0.34,0.258,0.369 5 | Client,0.171,0.264,0.188,0.289,0.191,0.296,0.207,0.314,0.247,0.347 6 | -------------------------------------------------------------------------------- /models/Client.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer, ConvLayer 5 | from layers.Autoformer_EncDec import moving_avg 6 | from layers.SelfAttention_Family import FullAttention, AttentionLayer, ProbAttention, DSAttention 7 | from layers.Embed import DataEmbedding 8 | import numpy as np 9 | from layers.RevIN import RevIN 10 | 11 | 12 | class Model(nn.Module): 13 | 14 | def __init__(self, configs): 15 | super(Model, self).__init__() 16 | self.pred_len = configs.pred_len 17 | self.output_attention = configs.output_attention 18 | configs.d_model = configs.seq_len 19 | self.encoder = Encoder( 20 | [ 21 | EncoderLayer( 22 | AttentionLayer( 23 | FullAttention(False, configs.factor, attention_dropout=configs.dropout, 24 | output_attention=configs.output_attention), configs.d_model, configs.n_heads), 25 | configs.d_model, 26 | configs.d_ff, 27 | dropout=configs.dropout, 28 | activation=configs.activation 29 | ) for l in range(configs.e_layers) 30 | ], 31 | norm_layer=torch.nn.LayerNorm(configs.d_model) 32 | ) 33 | self.proj = nn.Linear(configs.d_model, self.pred_len, bias=True) 34 | self.Linear = nn.Sequential() 35 | self.Linear.add_module('Linear',nn.Linear(configs.seq_len, self.pred_len)) 36 | self.w_dec = torch.nn.Parameter(torch.FloatTensor([configs.w_lin]*configs.enc_in),requires_grad=True) 37 | self.revin_layer = RevIN(configs.enc_in) 38 | 39 | 40 | def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, 41 | enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None): 42 | 43 | x_enc = self.revin_layer(x_enc, 'norm') 44 | enc_out = x_enc.permute(0, 2, 1) 45 | enc_out, attns = self.encoder(enc_out, attn_mask=enc_self_mask) 46 | dec_out = self.proj(enc_out) 47 | dec_out = dec_out.permute(0, 2, 1) 48 | linear_out = self.Linear(x_enc.permute(0,2,1)).permute(0,2,1) 49 | dec_out = self.revin_layer(dec_out[:, -self.pred_len:, :]+self.w_dec*linear_out, 'denorm') 50 | 51 | if self.output_attention: 52 | return dec_out[:, -self.pred_len:, :], attns 53 | else: 54 | return dec_out 55 | -------------------------------------------------------------------------------- /models/Linear.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | from layers.RevIN import RevIN 6 | 7 | class Model(nn.Module): 8 | """ 9 | Just one Linear layer 10 | """ 11 | def __init__(self, configs): 12 | super(Model, self).__init__() 13 | self.seq_len = configs.seq_len 14 | self.pred_len = configs.pred_len 15 | 16 | self.channels = configs.enc_in 17 | self.individual = False 18 | if self.individual: 19 | self.Linear = nn.ModuleList() 20 | for i in range(self.channels): 21 | self.Linear.append(nn.Linear(self.seq_len,self.pred_len)) 22 | else: 23 | self.Linear = nn.Linear(self.seq_len, self.pred_len) 24 | 25 | def forward(self, x, x_mark_enc, x_dec, x_mark_dec, mask=None): 26 | # x: [Batch, Input length, Channel] 27 | if self.individual: 28 | output = torch.zeros([x.size(0),self.pred_len,x.size(2)],dtype=x.dtype).to(x.device) 29 | for i in range(self.channels): 30 | output[:,:,i] = self.Linear[i](x[:,:,i]) 31 | x = output 32 | else: 33 | x = self.Linear(x.permute(0,2,1)).permute(0,2,1) 34 | return x # [Batch, Output length, Channel] 35 | -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daxin007/Client/802c603f5ff3dd7814afd3e096f9082873373f1e/models/__init__.py -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | einops==0.4.0 2 | matplotlib==3.7.0 3 | numpy==1.23.5 4 | pandas==1.5.3 5 | patool==1.12 6 | reformer-pytorch==1.4.4 7 | scikit-learn==1.2.2 8 | scipy==1.10.1 9 | sktime==0.16.1 10 | sympy==1.11.1 11 | torch==1.7.1 12 | tqdm==4.64.1 13 | -------------------------------------------------------------------------------- /scripts/ECL_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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 88 | -------------------------------------------------------------------------------- /scripts/ECL_script/Client.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 4 | seq_length=96 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/electricity/ \ 10 | --data_path electricity.csv \ 11 | --model_id ECL_96_96 \ 12 | --model $model_name \ 13 | --data custom \ 14 | --features M \ 15 | --seq_len $seq_length \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --factor 3 \ 21 | --enc_in 321 \ 22 | --dec_in 321 \ 23 | --c_out 321 \ 24 | --des 'Exp' \ 25 | --learning_rate 0.001 \ 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 M \ 37 | --seq_len $seq_length \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 321 \ 44 | --dec_in 321 \ 45 | --c_out 321 \ 46 | --des 'Exp' \ 47 | --learning_rate 0.001 \ 48 | --itr 1 49 | 50 | python -u run.py \ 51 | --task_name long_term_forecast \ 52 | --is_training 1 \ 53 | --root_path ./dataset/electricity/ \ 54 | --data_path electricity.csv \ 55 | --model_id ECL_96_336 \ 56 | --model $model_name \ 57 | --data custom \ 58 | --features M \ 59 | --seq_len $seq_length \ 60 | --label_len 48 \ 61 | --pred_len 336 \ 62 | --e_layers 2 \ 63 | --d_layers 1 \ 64 | --factor 3 \ 65 | --enc_in 321 \ 66 | --dec_in 321 \ 67 | --c_out 321 \ 68 | --des 'Exp' \ 69 | --learning_rate 0.001 \ 70 | --itr 1 71 | 72 | python -u run.py \ 73 | --task_name long_term_forecast \ 74 | --is_training 1 \ 75 | --root_path ./dataset/electricity/ \ 76 | --data_path electricity.csv \ 77 | --model_id ECL_96_720 \ 78 | --model $model_name \ 79 | --data custom \ 80 | --features M \ 81 | --seq_len $seq_length \ 82 | --label_len 48 \ 83 | --pred_len 720 \ 84 | --e_layers 2 \ 85 | --d_layers 1 \ 86 | --factor 3 \ 87 | --enc_in 321 \ 88 | --dec_in 321 \ 89 | --c_out 321 \ 90 | --des 'Exp' \ 91 | --learning_rate 0.001 \ 92 | --itr 1 93 | -------------------------------------------------------------------------------- /scripts/ECL_script/Crossformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 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/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 | --d_model 256 \ 24 | --d_ff 512 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --batch_size 16 \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/electricity/ \ 34 | --data_path electricity.csv \ 35 | --model_id ECL_96_192 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len 96 \ 40 | --label_len 48 \ 41 | --pred_len 192 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 321 \ 46 | --dec_in 321 \ 47 | --c_out 321 \ 48 | --d_model 256 \ 49 | --d_ff 512 \ 50 | --top_k 5 \ 51 | --des 'Exp' \ 52 | --batch_size 16 \ 53 | --itr 1 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/electricity/ \ 59 | --data_path electricity.csv \ 60 | --model_id ECL_96_336 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 48 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 321 \ 71 | --dec_in 321 \ 72 | --c_out 321 \ 73 | --d_model 256 \ 74 | --d_ff 512 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | --batch_size 16 \ 78 | --itr 1 79 | 80 | python -u run.py \ 81 | --task_name long_term_forecast \ 82 | --is_training 1 \ 83 | --root_path ./dataset/electricity/ \ 84 | --data_path electricity.csv \ 85 | --model_id ECL_96_720 \ 86 | --model $model_name \ 87 | --data custom \ 88 | --features M \ 89 | --seq_len 96 \ 90 | --label_len 48 \ 91 | --pred_len 720 \ 92 | --e_layers 2 \ 93 | --d_layers 1 \ 94 | --factor 3 \ 95 | --enc_in 321 \ 96 | --dec_in 321 \ 97 | --c_out 321 \ 98 | --d_model 256 \ 99 | --d_ff 512 \ 100 | --top_k 5 \ 101 | --des 'Exp' \ 102 | --batch_size 16 \ 103 | --itr 1 -------------------------------------------------------------------------------- /scripts/ECL_script/DLinear.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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 88 | -------------------------------------------------------------------------------- /scripts/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/ECL_script/FEDformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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/ECL_script/Informer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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 88 | -------------------------------------------------------------------------------- /scripts/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/ECL_script/Linear.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Linear 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 | --learning_rate 0.001 \ 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 | --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 2 \ 61 | --d_layers 1 \ 62 | --factor 3 \ 63 | --enc_in 321 \ 64 | --dec_in 321 \ 65 | --c_out 321 \ 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/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 2 \ 82 | --d_layers 1 \ 83 | --factor 3 \ 84 | --enc_in 321 \ 85 | --dec_in 321 \ 86 | --c_out 321 \ 87 | --des 'Exp' \ 88 | --itr 1 89 | -------------------------------------------------------------------------------- /scripts/ECL_script/MICN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 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/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 96 \ 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 | --d_model 256 \ 24 | --d_ff 512 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/electricity/ \ 33 | --data_path electricity.csv \ 34 | --model_id ECL_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 96 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 321 \ 45 | --dec_in 321 \ 46 | --c_out 321 \ 47 | --d_model 256 \ 48 | --d_ff 512 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/electricity/ \ 57 | --data_path electricity.csv \ 58 | --model_id ECL_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 96 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 321 \ 69 | --dec_in 321 \ 70 | --c_out 321 \ 71 | --d_model 256 \ 72 | --d_ff 512 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/electricity/ \ 81 | --data_path electricity.csv \ 82 | --model_id ECL_96_720 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 96 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 321 \ 93 | --dec_in 321 \ 94 | --c_out 321 \ 95 | --d_model 256 \ 96 | --d_ff 512 \ 97 | --top_k 5 \ 98 | --des 'Exp' \ 99 | --itr 1 -------------------------------------------------------------------------------- /scripts/ECL_script/Nonstationary_Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 256 256 \ 26 | --p_hidden_layers 2 \ 27 | --d_model 2048 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/electricity/ \ 33 | --data_path electricity.csv \ 34 | --model_id ECL_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features S \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 1 \ 45 | --dec_in 1 \ 46 | --c_out 1 \ 47 | --des 'Exp' \ 48 | --itr 1 \ 49 | --p_hidden_dims 256 256 \ 50 | --p_hidden_layers 2 \ 51 | --d_model 2048 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/electricity/ \ 57 | --data_path electricity.csv \ 58 | --model_id ECL_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features S \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 1 \ 69 | --dec_in 1 \ 70 | --c_out 1 \ 71 | --des 'Exp' \ 72 | --itr 1 \ 73 | --p_hidden_dims 256 256 \ 74 | --p_hidden_layers 2 \ 75 | --d_model 2048 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/electricity/ \ 81 | --data_path electricity.csv \ 82 | --model_id ECL_96_720 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features S \ 86 | --seq_len 96 \ 87 | --label_len 48 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 1 \ 93 | --dec_in 1 \ 94 | --c_out 1 \ 95 | --des 'Exp' \ 96 | --itr 1 \ 97 | --p_hidden_dims 256 256 \ 98 | --p_hidden_layers 2 \ 99 | --d_model 2048 -------------------------------------------------------------------------------- /scripts/ECL_script/PatchTST.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/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 92 | -------------------------------------------------------------------------------- /scripts/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/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/ECL_script/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimesNet 4 | seq_length=96 5 | 6 | python -u run.py \ 7 | --task_name long_term_forecast \ 8 | --is_training 1 \ 9 | --root_path ./dataset/electricity/ \ 10 | --data_path electricity.csv \ 11 | --model_id ECL_96_96 \ 12 | --model $model_name \ 13 | --data custom \ 14 | --features M \ 15 | --seq_len $seq_length \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --factor 3 \ 21 | --enc_in 321 \ 22 | --dec_in 321 \ 23 | --c_out 321 \ 24 | --d_model 256 \ 25 | --d_ff 512 \ 26 | --top_k 5 \ 27 | --des 'Exp' \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/electricity/ \ 34 | --data_path electricity.csv \ 35 | --model_id ECL_96_192 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len $seq_length \ 40 | --label_len 48 \ 41 | --pred_len 192 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 321 \ 46 | --dec_in 321 \ 47 | --c_out 321 \ 48 | --d_model 256 \ 49 | --d_ff 512 \ 50 | --top_k 5 \ 51 | --des 'Exp' \ 52 | --itr 1 53 | 54 | python -u run.py \ 55 | --task_name long_term_forecast \ 56 | --is_training 1 \ 57 | --root_path ./dataset/electricity/ \ 58 | --data_path electricity.csv \ 59 | --model_id ECL_96_336 \ 60 | --model $model_name \ 61 | --data custom \ 62 | --features M \ 63 | --seq_len $seq_length \ 64 | --label_len 48 \ 65 | --pred_len 336 \ 66 | --e_layers 2 \ 67 | --d_layers 1 \ 68 | --factor 3 \ 69 | --enc_in 321 \ 70 | --dec_in 321 \ 71 | --c_out 321 \ 72 | --d_model 256 \ 73 | --d_ff 512 \ 74 | --top_k 5 \ 75 | --des 'Exp' \ 76 | --itr 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/electricity/ \ 82 | --data_path electricity.csv \ 83 | --model_id ECL_96_720 \ 84 | --model $model_name \ 85 | --data custom \ 86 | --features M \ 87 | --seq_len $seq_length \ 88 | --label_len 48 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 321 \ 94 | --dec_in 321 \ 95 | --c_out 321 \ 96 | --d_model 256 \ 97 | --d_ff 512 \ 98 | --top_k 5 \ 99 | --des 'Exp' \ 100 | --itr 1 101 | -------------------------------------------------------------------------------- /scripts/ECL_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/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 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 88 | -------------------------------------------------------------------------------- /scripts/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/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/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/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/ETT_script/Client_ETTh1.sh: -------------------------------------------------------------------------------- 1 | model_name=Client 2 | 3 | python -u run.py \ 4 | --task_name long_term_forecast \ 5 | --is_training 1 \ 6 | --root_path ./dataset/ETT-small/ \ 7 | --data_path ETTh1.csv \ 8 | --model_id ETTh1_96_96 \ 9 | --model $model_name \ 10 | --data ETTh1 \ 11 | --features M \ 12 | --seq_len 96 \ 13 | --label_len 48 \ 14 | --pred_len 96 \ 15 | --e_layers 2 \ 16 | --d_layers 1 \ 17 | --factor 3 \ 18 | --enc_in 7 \ 19 | --dec_in 7 \ 20 | --c_out 7 \ 21 | --d_model 16 \ 22 | --d_ff 32 \ 23 | --des 'Exp' \ 24 | --learning_rate 0.0001 \ 25 | --w_lin 0.05 \ 26 | --itr 1 27 | 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTh1.csv \ 34 | --model_id ETTh1_96_192 \ 35 | --model $model_name \ 36 | --data ETTh1 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --d_model 16 \ 48 | --d_ff 32 \ 49 | --des 'Exp' \ 50 | --learning_rate 0.0001 \ 51 | --w_lin 0.5 \ 52 | --itr 1 53 | 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/ETT-small/ \ 59 | --data_path ETTh1.csv \ 60 | --model_id ETTh1_96_336 \ 61 | --model $model_name \ 62 | --data ETTh1 \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 48 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 7 \ 71 | --dec_in 7 \ 72 | --c_out 7 \ 73 | --d_model 16 \ 74 | --d_ff 32 \ 75 | --des 'Exp' \ 76 | --learning_rate 0.0001 \ 77 | --w_lin 0.5 \ 78 | --itr 1 79 | 80 | 81 | python -u run.py \ 82 | --task_name long_term_forecast \ 83 | --is_training 1 \ 84 | --root_path ./dataset/ETT-small/ \ 85 | --data_path ETTh1.csv \ 86 | --model_id ETTh1_96_720 \ 87 | --model $model_name \ 88 | --data ETTh1 \ 89 | --features M \ 90 | --seq_len 96 \ 91 | --label_len 48 \ 92 | --pred_len 720 \ 93 | --e_layers 2 \ 94 | --d_layers 1 \ 95 | --factor 3 \ 96 | --enc_in 7 \ 97 | --dec_in 7 \ 98 | --c_out 7 \ 99 | --d_model 16 \ 100 | --d_ff 32 \ 101 | --des 'Exp' \ 102 | --learning_rate 0.0001 \ 103 | --w_lin 0.5 \ 104 | --itr 1 105 | -------------------------------------------------------------------------------- /scripts/ETT_script/Client_ETTh2.sh: -------------------------------------------------------------------------------- 1 | 2 | model_name=Client 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 | --d_model 16 \ 23 | --d_ff 32 \ 24 | --des 'Exp' \ 25 | --learning_rate 0.001 \ 26 | --itr 1 27 | 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTh2.csv \ 34 | --model_id ETTh2_96_192 \ 35 | --model $model_name \ 36 | --data ETTh2 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --d_model 16 \ 48 | --d_ff 32 \ 49 | --des 'Exp' \ 50 | --learning_rate 0.001 \ 51 | --itr 1 52 | 53 | 54 | python -u run.py \ 55 | --task_name long_term_forecast \ 56 | --is_training 1 \ 57 | --root_path ./dataset/ETT-small/ \ 58 | --data_path ETTh2.csv \ 59 | --model_id ETTh2_96_336 \ 60 | --model $model_name \ 61 | --data ETTh2 \ 62 | --features M \ 63 | --seq_len 96 \ 64 | --label_len 48 \ 65 | --pred_len 336 \ 66 | --e_layers 2 \ 67 | --d_layers 1 \ 68 | --factor 3 \ 69 | --enc_in 7 \ 70 | --dec_in 7 \ 71 | --c_out 7 \ 72 | --d_model 16 \ 73 | --d_ff 32 \ 74 | --des 'Exp' \ 75 | --learning_rate 0.001 \ 76 | --itr 1 77 | 78 | 79 | python -u run.py \ 80 | --task_name long_term_forecast \ 81 | --is_training 1 \ 82 | --root_path ./dataset/ETT-small/ \ 83 | --data_path ETTh2.csv \ 84 | --model_id ETTh2_96_720 \ 85 | --model $model_name \ 86 | --data ETTh2 \ 87 | --features M \ 88 | --seq_len 96 \ 89 | --label_len 48 \ 90 | --pred_len 720 \ 91 | --e_layers 2 \ 92 | --d_layers 1 \ 93 | --factor 3 \ 94 | --enc_in 7 \ 95 | --dec_in 7 \ 96 | --c_out 7 \ 97 | --d_model 16 \ 98 | --d_ff 32 \ 99 | --des 'Exp' \ 100 | --learning_rate 0.001 \ 101 | --itr 1 102 | -------------------------------------------------------------------------------- /scripts/ETT_script/Client_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 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 | --d_model 64 \ 25 | --d_ff 64 \ 26 | --learning_rate 0.001 \ 27 | --w_lin 0.5 \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/ETT-small/ \ 34 | --data_path ETTm1.csv \ 35 | --model_id ETTm1_96_192 \ 36 | --model $model_name \ 37 | --data ETTm1 \ 38 | --features M \ 39 | --seq_len 96 \ 40 | --label_len 48 \ 41 | --pred_len 192 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 7 \ 46 | --dec_in 7 \ 47 | --c_out 7 \ 48 | --des 'Exp' \ 49 | --d_model 64 \ 50 | --d_ff 64 \ 51 | --learning_rate 0.005 \ 52 | --w_lin 0.5 \ 53 | --itr 1 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/ETT-small/ \ 59 | --data_path ETTm1.csv \ 60 | --model_id ETTm1_96_336 \ 61 | --model $model_name \ 62 | --data ETTm1 \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 48 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 7 \ 71 | --dec_in 7 \ 72 | --c_out 7 \ 73 | --des 'Exp' \ 74 | --d_model 16 \ 75 | --d_ff 32 \ 76 | --learning_rate 0.005 \ 77 | --itr 1 \ 78 | --w_lin 0.5 \ 79 | --train_epochs 10 80 | 81 | python -u run.py \ 82 | --task_name long_term_forecast \ 83 | --is_training 1 \ 84 | --root_path ./dataset/ETT-small/ \ 85 | --data_path ETTm1.csv \ 86 | --model_id ETTm1_96_720 \ 87 | --model $model_name \ 88 | --data ETTm1 \ 89 | --features M \ 90 | --seq_len 96 \ 91 | --label_len 48 \ 92 | --pred_len 720 \ 93 | --e_layers 2 \ 94 | --d_layers 1 \ 95 | --factor 3 \ 96 | --enc_in 7 \ 97 | --dec_in 7 \ 98 | --c_out 7 \ 99 | --des 'Exp' \ 100 | --d_model 16 \ 101 | --d_ff 32 \ 102 | --learning_rate 0.005 \ 103 | --itr 1 \ 104 | --w_lin 0.5 \ 105 | --train_epochs 10 106 | -------------------------------------------------------------------------------- /scripts/ETT_script/Client_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 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 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --d_model 32 \ 25 | --d_ff 32 \ 26 | --learning_rate 0.001 \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTm2.csv \ 34 | --model_id ETTm2_96_192 \ 35 | --model $model_name \ 36 | --data ETTm2 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --des 'Exp' \ 48 | --d_model 32 \ 49 | --d_ff 32 \ 50 | --itr 1 \ 51 | --learning_rate 0.001 \ 52 | --train_epochs 10 53 | 54 | python -u run.py \ 55 | --task_name long_term_forecast \ 56 | --is_training 1 \ 57 | --root_path ./dataset/ETT-small/ \ 58 | --data_path ETTm2.csv \ 59 | --model_id ETTm2_96_336 \ 60 | --model $model_name \ 61 | --data ETTm2 \ 62 | --features M \ 63 | --seq_len 96 \ 64 | --label_len 48 \ 65 | --pred_len 336 \ 66 | --e_layers 2 \ 67 | --d_layers 1 \ 68 | --factor 3 \ 69 | --enc_in 7 \ 70 | --dec_in 7 \ 71 | --c_out 7 \ 72 | --des 'Exp' \ 73 | --d_model 32 \ 74 | --d_ff 32 \ 75 | --learning_rate 0.001 \ 76 | --itr 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/ETT-small/ \ 82 | --data_path ETTm2.csv \ 83 | --model_id ETTm2_96_720 \ 84 | --model $model_name \ 85 | --data ETTm2 \ 86 | --features M \ 87 | --seq_len 96 \ 88 | --label_len 48 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 7 \ 94 | --dec_in 7 \ 95 | --c_out 7 \ 96 | --des 'Exp' \ 97 | --d_model 16 \ 98 | --d_ff 32 \ 99 | --itr 1 \ 100 | --learning_rate 0.001 \ 101 | --train_epochs 10 102 | -------------------------------------------------------------------------------- /scripts/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/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/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/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/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/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/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/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/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/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 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/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 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/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/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/ETT_script/Nonstationary_Transformer_ETTh1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 256 256 \ 26 | --p_hidden_layers 2 \ 27 | --d_model 128 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTh1.csv \ 34 | --model_id ETTh1_96_192 \ 35 | --model $model_name \ 36 | --data ETTh1 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --des 'Exp' \ 48 | --itr 1 \ 49 | --p_hidden_dims 256 256 \ 50 | --p_hidden_layers 2 \ 51 | --d_model 128 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/ETT-small/ \ 57 | --data_path ETTh1.csv \ 58 | --model_id ETTh1_96_336 \ 59 | --model $model_name \ 60 | --data ETTh1 \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 7 \ 69 | --dec_in 7 \ 70 | --c_out 7 \ 71 | --des 'Exp' \ 72 | --itr 1 \ 73 | --p_hidden_dims 256 256 \ 74 | --p_hidden_layers 2 \ 75 | --d_model 128 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/ETT-small/ \ 81 | --data_path ETTh1.csv \ 82 | --model_id ETTh1_96_720 \ 83 | --model $model_name \ 84 | --data ETTh1 \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 48 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 7 \ 93 | --dec_in 7 \ 94 | --c_out 7 \ 95 | --des 'Exp' \ 96 | --itr 1 \ 97 | --p_hidden_dims 256 256 \ 98 | --p_hidden_layers 2 \ 99 | --d_model 128 -------------------------------------------------------------------------------- /scripts/ETT_script/Nonstationary_Transformer_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 2 | 3 | model_name=Nonstationary_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 1 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --itr 1 \ 25 | --p_hidden_dims 256 256 \ 26 | --p_hidden_layers 2 \ 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 ETTh2.csv \ 33 | --model_id ETTh2_96_192 \ 34 | --model $model_name \ 35 | --data ETTh2 \ 36 | --features M \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 1 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --itr 1 \ 48 | --p_hidden_dims 256 256 \ 49 | --p_hidden_layers 2 \ 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 ETTh2.csv \ 56 | --model_id ETTh2_96_336 \ 57 | --model $model_name \ 58 | --data ETTh2 \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 1 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --itr 1 \ 71 | --p_hidden_dims 256 256 256 256 \ 72 | --p_hidden_layers 4 \ 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 ETTh2.csv \ 79 | --model_id ETTh2_96_720 \ 80 | --model $model_name \ 81 | --data ETTh2 \ 82 | --features M \ 83 | --seq_len 96 \ 84 | --label_len 48 \ 85 | --pred_len 720 \ 86 | --e_layers 2 \ 87 | --d_layers 1 \ 88 | --factor 1 \ 89 | --enc_in 7 \ 90 | --dec_in 7 \ 91 | --c_out 7 \ 92 | --des 'Exp' \ 93 | --itr 1 \ 94 | --p_hidden_dims 256 256 \ 95 | --p_hidden_layers 2 \ 96 | -------------------------------------------------------------------------------- /scripts/ETT_script/Nonstationary_Transformer_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=3 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 16 16 16 16 \ 25 | --p_hidden_layers 4 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 48 \ 38 | --pred_len 192 \ 39 | --e_layers 2 \ 40 | --d_layers 1 \ 41 | --enc_in 7 \ 42 | --dec_in 7 \ 43 | --c_out 7 \ 44 | --des 'Exp' \ 45 | --itr 1 \ 46 | --p_hidden_dims 16 16 16 16 \ 47 | --p_hidden_layers 4 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 48 \ 60 | --pred_len 336 \ 61 | --e_layers 2 \ 62 | --d_layers 1 \ 63 | --enc_in 7 \ 64 | --dec_in 7 \ 65 | --c_out 7 \ 66 | --des 'Exp' \ 67 | --itr 1 \ 68 | --p_hidden_dims 16 16 16 16 \ 69 | --p_hidden_layers 4 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 48 \ 82 | --pred_len 720 \ 83 | --e_layers 2 \ 84 | --d_layers 1 \ 85 | --enc_in 7 \ 86 | --dec_in 7 \ 87 | --c_out 7 \ 88 | --des 'Exp' \ 89 | --itr 1 \ 90 | --p_hidden_dims 16 16 16 16 \ 91 | --p_hidden_layers 4 -------------------------------------------------------------------------------- /scripts/ETT_script/Nonstationary_Transformer_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 256 256 256 256 \ 26 | --p_hidden_layers 4 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 M \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 1 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --itr 1 \ 48 | --p_hidden_dims 256 256 256 256 \ 49 | --p_hidden_layers 4 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 ETTm2.csv \ 56 | --model_id ETTm2_96_336 \ 57 | --model $model_name \ 58 | --data ETTm2 \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 1 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --itr 1 \ 71 | --p_hidden_dims 16 16 16 16 \ 72 | --p_hidden_layers 4 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 ETTm2.csv \ 79 | --model_id ETTm2_96_720 \ 80 | --model $model_name \ 81 | --data ETTm2 \ 82 | --features M \ 83 | --seq_len 96 \ 84 | --label_len 48 \ 85 | --pred_len 720 \ 86 | --e_layers 2 \ 87 | --d_layers 1 \ 88 | --factor 1 \ 89 | --enc_in 7 \ 90 | --dec_in 7 \ 91 | --c_out 7 \ 92 | --des 'Exp' \ 93 | --itr 1 \ 94 | --p_hidden_dims 16 16 16 16 \ 95 | --p_hidden_layers 4 -------------------------------------------------------------------------------- /scripts/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/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/ETT_script/PatchTST_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 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 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 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 | --batch_size 32 \ 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 M \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 3 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --n_heads 2 \ 48 | --batch_size 128 \ 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 ETTm1.csv \ 56 | --model_id ETTm1_96_336 \ 57 | --model $model_name \ 58 | --data ETTm1 \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 1 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --n_heads 4 \ 71 | --batch_size 128 \ 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 ETTm1.csv \ 79 | --model_id ETTm1_96_720 \ 80 | --model $model_name \ 81 | --data ETTm1 \ 82 | --features M \ 83 | --seq_len 96 \ 84 | --label_len 48 \ 85 | --pred_len 720 \ 86 | --e_layers 3 \ 87 | --d_layers 1 \ 88 | --factor 3 \ 89 | --enc_in 7 \ 90 | --dec_in 7 \ 91 | --c_out 7 \ 92 | --des 'Exp' \ 93 | --n_heads 4 \ 94 | --batch_size 128 \ 95 | --itr 1 -------------------------------------------------------------------------------- /scripts/ETT_script/PatchTST_ETTm2.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/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 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 16 \ 25 | --batch_size 32 \ 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 M \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 3 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --n_heads 2 \ 48 | --batch_size 128 \ 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 ETTm2.csv \ 56 | --model_id ETTm2_96_336 \ 57 | --model $model_name \ 58 | --data ETTm2 \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 1 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --n_heads 4 \ 71 | --batch_size 32 \ 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 ETTm2.csv \ 79 | --model_id ETTm2_96_720 \ 80 | --model $model_name \ 81 | --data ETTm2 \ 82 | --features M \ 83 | --seq_len 96 \ 84 | --label_len 48 \ 85 | --pred_len 720 \ 86 | --e_layers 3 \ 87 | --d_layers 1 \ 88 | --factor 3 \ 89 | --enc_in 7 \ 90 | --dec_in 7 \ 91 | --c_out 7 \ 92 | --des 'Exp' \ 93 | --n_heads 4 \ 94 | --batch_size 128 \ 95 | --itr 1 -------------------------------------------------------------------------------- /scripts/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/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/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/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/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/ETT_script/TimesNet_ETTh1.sh: -------------------------------------------------------------------------------- 1 | 2 | model_name=TimesNet 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 | --d_model 16 \ 23 | --d_ff 32 \ 24 | --des 'Exp' \ 25 | --itr 1 \ 26 | --top_k 5 27 | 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTh1.csv \ 34 | --model_id ETTh1_96_192 \ 35 | --model $model_name \ 36 | --data ETTh1 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --d_model 16 \ 48 | --d_ff 32 \ 49 | --des 'Exp' \ 50 | --itr 1 \ 51 | --top_k 5 52 | 53 | 54 | python -u run.py \ 55 | --task_name long_term_forecast \ 56 | --is_training 1 \ 57 | --root_path ./dataset/ETT-small/ \ 58 | --data_path ETTh1.csv \ 59 | --model_id ETTh1_96_336 \ 60 | --model $model_name \ 61 | --data ETTh1 \ 62 | --features M \ 63 | --seq_len 96 \ 64 | --label_len 48 \ 65 | --pred_len 336 \ 66 | --e_layers 2 \ 67 | --d_layers 1 \ 68 | --factor 3 \ 69 | --enc_in 7 \ 70 | --dec_in 7 \ 71 | --c_out 7 \ 72 | --d_model 16 \ 73 | --d_ff 32 \ 74 | --des 'Exp' \ 75 | --itr 1 \ 76 | --top_k 5 77 | 78 | 79 | python -u run.py \ 80 | --task_name long_term_forecast \ 81 | --is_training 1 \ 82 | --root_path ./dataset/ETT-small/ \ 83 | --data_path ETTh1.csv \ 84 | --model_id ETTh1_96_720 \ 85 | --model $model_name \ 86 | --data ETTh1 \ 87 | --features M \ 88 | --seq_len 96 \ 89 | --label_len 48 \ 90 | --pred_len 720 \ 91 | --e_layers 2 \ 92 | --d_layers 1 \ 93 | --factor 3 \ 94 | --enc_in 7 \ 95 | --dec_in 7 \ 96 | --c_out 7 \ 97 | --d_model 16 \ 98 | --d_ff 32 \ 99 | --des 'Exp' \ 100 | --itr 1 \ 101 | --top_k 5 -------------------------------------------------------------------------------- /scripts/ETT_script/TimesNet_ETTh2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | model_name=TimesNet 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 | --d_model 32 \ 24 | --d_ff 32 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTh2.csv \ 34 | --model_id ETTh2_96_192 \ 35 | --model $model_name \ 36 | --data ETTh2 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --d_model 32 \ 48 | --d_ff 32 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/ETT-small/ \ 57 | --data_path ETTh2.csv \ 58 | --model_id ETTh2_96_336 \ 59 | --model $model_name \ 60 | --data ETTh2 \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 7 \ 69 | --dec_in 7 \ 70 | --c_out 7 \ 71 | --d_model 32 \ 72 | --d_ff 32 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/ETT-small/ \ 81 | --data_path ETTh2.csv \ 82 | --model_id ETTh2_96_720 \ 83 | --model $model_name \ 84 | --data ETTh2 \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 48 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 7 \ 93 | --dec_in 7 \ 94 | --c_out 7 \ 95 | --d_model 32 \ 96 | --d_ff 32 \ 97 | --top_k 5 \ 98 | --des 'Exp' \ 99 | --itr 1 100 | -------------------------------------------------------------------------------- /scripts/ETT_script/TimesNet_ETTm1.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimesNet 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 | --d_model 64 \ 25 | --d_ff 64 \ 26 | --top_k 5 \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTm1.csv \ 34 | --model_id ETTm1_96_192 \ 35 | --model $model_name \ 36 | --data ETTm1 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --des 'Exp' \ 48 | --d_model 64 \ 49 | --d_ff 64 \ 50 | --top_k 5 \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/ETT-small/ \ 57 | --data_path ETTm1.csv \ 58 | --model_id ETTm1_96_336 \ 59 | --model $model_name \ 60 | --data ETTm1 \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 7 \ 69 | --dec_in 7 \ 70 | --c_out 7 \ 71 | --des 'Exp' \ 72 | --d_model 16 \ 73 | --d_ff 32 \ 74 | --top_k 5 \ 75 | --itr 1 \ 76 | --train_epochs 3 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/ETT-small/ \ 82 | --data_path ETTm1.csv \ 83 | --model_id ETTm1_96_720 \ 84 | --model $model_name \ 85 | --data ETTm1 \ 86 | --features M \ 87 | --seq_len 96 \ 88 | --label_len 48 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 7 \ 94 | --dec_in 7 \ 95 | --c_out 7 \ 96 | --des 'Exp' \ 97 | --d_model 16 \ 98 | --d_ff 32 \ 99 | --top_k 5 \ 100 | --itr 1 -------------------------------------------------------------------------------- /scripts/ETT_script/TimesNet_ETTm2.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=1 2 | 3 | model_name=TimesNet 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 3 \ 20 | --enc_in 7 \ 21 | --dec_in 7 \ 22 | --c_out 7 \ 23 | --des 'Exp' \ 24 | --d_model 32 \ 25 | --d_ff 32 \ 26 | --top_k 5 \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/ETT-small/ \ 33 | --data_path ETTm2.csv \ 34 | --model_id ETTm2_96_192 \ 35 | --model $model_name \ 36 | --data ETTm2 \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --des 'Exp' \ 48 | --d_model 32 \ 49 | --d_ff 32 \ 50 | --top_k 5 \ 51 | --itr 1 \ 52 | --train_epochs 1 53 | 54 | python -u run.py \ 55 | --task_name long_term_forecast \ 56 | --is_training 1 \ 57 | --root_path ./dataset/ETT-small/ \ 58 | --data_path ETTm2.csv \ 59 | --model_id ETTm2_96_336 \ 60 | --model $model_name \ 61 | --data ETTm2 \ 62 | --features M \ 63 | --seq_len 96 \ 64 | --label_len 48 \ 65 | --pred_len 336 \ 66 | --e_layers 2 \ 67 | --d_layers 1 \ 68 | --factor 3 \ 69 | --enc_in 7 \ 70 | --dec_in 7 \ 71 | --c_out 7 \ 72 | --des 'Exp' \ 73 | --d_model 32 \ 74 | --d_ff 32 \ 75 | --top_k 5 \ 76 | --itr 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/ETT-small/ \ 82 | --data_path ETTm2.csv \ 83 | --model_id ETTm2_96_720 \ 84 | --model $model_name \ 85 | --data ETTm2 \ 86 | --features M \ 87 | --seq_len 96 \ 88 | --label_len 48 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 7 \ 94 | --dec_in 7 \ 95 | --c_out 7 \ 96 | --des 'Exp' \ 97 | --d_model 16 \ 98 | --d_ff 32 \ 99 | --top_k 5 \ 100 | --itr 1 \ 101 | --train_epochs 1 -------------------------------------------------------------------------------- /scripts/ETT_script/Transformer_ETTh1.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 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 88 | -------------------------------------------------------------------------------- /scripts/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/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/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/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/Exchange_script/Client.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 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 2 \ 20 | --enc_in 8 \ 21 | --dec_in 8 \ 22 | --c_out 8 \ 23 | --des 'Exp' \ 24 | --learning_rate 0.00005 \ 25 | --itr 1 26 | 27 | python -u run.py \ 28 | --task_name long_term_forecast \ 29 | --is_training 1 \ 30 | --root_path ./dataset/exchange_rate/ \ 31 | --data_path exchange_rate.csv \ 32 | --model_id Exchange_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 8 \ 43 | --dec_in 8 \ 44 | --c_out 8 \ 45 | --des 'Exp' \ 46 | --learning_rate 0.00005 \ 47 | --itr 1 48 | 49 | python -u run.py \ 50 | --task_name long_term_forecast \ 51 | --is_training 1 \ 52 | --root_path ./dataset/exchange_rate/ \ 53 | --data_path exchange_rate.csv \ 54 | --model_id Exchange_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 8 \ 65 | --dec_in 8 \ 66 | --c_out 8 \ 67 | --des 'Exp' \ 68 | --learning_rate 0.0001 \ 69 | --itr 1 70 | 71 | python -u run.py \ 72 | --task_name long_term_forecast \ 73 | --is_training 1 \ 74 | --root_path ./dataset/exchange_rate/ \ 75 | --data_path exchange_rate.csv \ 76 | --model_id Exchange_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 8 \ 87 | --dec_in 8 \ 88 | --c_out 8 \ 89 | --des 'Exp' \ 90 | --learning_rate 0.00005 \ 91 | --itr 1 92 | -------------------------------------------------------------------------------- /scripts/Exchange_script/Crossformer.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/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 96 \ 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 | --d_model 64 \ 24 | --d_ff 64 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/exchange_rate/ \ 33 | --data_path exchange_rate.csv \ 34 | --model_id Exchange_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 96 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 8 \ 45 | --dec_in 8 \ 46 | --c_out 8 \ 47 | --d_model 64 \ 48 | --d_ff 64 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/exchange_rate/ \ 57 | --data_path exchange_rate.csv \ 58 | --model_id Exchange_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 96 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 8 \ 69 | --dec_in 8 \ 70 | --c_out 8 \ 71 | --d_model 32 \ 72 | --d_ff 32 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 \ 76 | --train_epochs 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/exchange_rate/ \ 82 | --data_path exchange_rate.csv \ 83 | --model_id Exchange_96_720 \ 84 | --model $model_name \ 85 | --data custom \ 86 | --features M \ 87 | --seq_len 96 \ 88 | --label_len 96 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 8 \ 94 | --dec_in 8 \ 95 | --c_out 8 \ 96 | --d_model 32 \ 97 | --d_ff 32 \ 98 | --top_k 5 \ 99 | --des 'Exp' \ 100 | --itr 1 \ 101 | --train_epochs 1 102 | -------------------------------------------------------------------------------- /scripts/Exchange_script/MICN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 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/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 96 \ 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 | --d_model 64 \ 24 | --d_ff 64 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/exchange_rate/ \ 33 | --data_path exchange_rate.csv \ 34 | --model_id Exchange_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 96 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 8 \ 45 | --dec_in 8 \ 46 | --c_out 8 \ 47 | --d_model 64 \ 48 | --d_ff 64 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/exchange_rate/ \ 57 | --data_path exchange_rate.csv \ 58 | --model_id Exchange_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 96 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 8 \ 69 | --dec_in 8 \ 70 | --c_out 8 \ 71 | --d_model 32 \ 72 | --d_ff 32 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 \ 76 | --train_epochs 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/exchange_rate/ \ 82 | --data_path exchange_rate.csv \ 83 | --model_id Exchange_96_720 \ 84 | --model $model_name \ 85 | --data custom \ 86 | --features M \ 87 | --seq_len 96 \ 88 | --label_len 96 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 8 \ 94 | --dec_in 8 \ 95 | --c_out 8 \ 96 | --d_model 32 \ 97 | --d_ff 32 \ 98 | --top_k 5 \ 99 | --des 'Exp' \ 100 | --itr 1 \ 101 | --train_epochs 1 102 | -------------------------------------------------------------------------------- /scripts/Exchange_script/Nonstationary_Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 256 256 \ 26 | --p_hidden_layers 2 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 96 \ 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 | --p_hidden_dims 64 64 64 64 \ 49 | --p_hidden_layers 4 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/exchange_rate/ \ 55 | --data_path exchange_rate.csv \ 56 | --model_id Exchange_96_336 \ 57 | --model $model_name \ 58 | --data custom \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 8 \ 67 | --dec_in 8 \ 68 | --c_out 8 \ 69 | --des 'Exp' \ 70 | --itr 1 \ 71 | --train_epochs 1 \ 72 | --p_hidden_dims 256 256 \ 73 | --p_hidden_layers 2 74 | 75 | python -u run.py \ 76 | --task_name long_term_forecast \ 77 | --is_training 1 \ 78 | --root_path ./dataset/exchange_rate/ \ 79 | --data_path exchange_rate.csv \ 80 | --model_id Exchange_96_720 \ 81 | --model $model_name \ 82 | --data custom \ 83 | --features M \ 84 | --seq_len 96 \ 85 | --label_len 48 \ 86 | --pred_len 720 \ 87 | --e_layers 2 \ 88 | --d_layers 1 \ 89 | --factor 3 \ 90 | --enc_in 8 \ 91 | --dec_in 8 \ 92 | --c_out 8 \ 93 | --des 'Exp' \ 94 | --itr 1 \ 95 | --p_hidden_dims 256 256 \ 96 | --p_hidden_layers 2 -------------------------------------------------------------------------------- /scripts/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/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/Exchange_script/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimesNet 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 | --d_model 64 \ 24 | --d_ff 64 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/exchange_rate/ \ 33 | --data_path exchange_rate.csv \ 34 | --model_id Exchange_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 8 \ 45 | --dec_in 8 \ 46 | --c_out 8 \ 47 | --d_model 64 \ 48 | --d_ff 64 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/exchange_rate/ \ 57 | --data_path exchange_rate.csv \ 58 | --model_id Exchange_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 8 \ 69 | --dec_in 8 \ 70 | --c_out 8 \ 71 | --d_model 32 \ 72 | --d_ff 32 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 \ 76 | --train_epochs 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/exchange_rate/ \ 82 | --data_path exchange_rate.csv \ 83 | --model_id Exchange_96_720 \ 84 | --model $model_name \ 85 | --data custom \ 86 | --features M \ 87 | --seq_len 96 \ 88 | --label_len 48 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 8 \ 94 | --dec_in 8 \ 95 | --c_out 8 \ 96 | --d_model 32 \ 97 | --d_ff 32 \ 98 | --top_k 5 \ 99 | --des 'Exp' \ 100 | --itr 1 \ 101 | --train_epochs 1 102 | -------------------------------------------------------------------------------- /scripts/Exchange_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/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 89 | -------------------------------------------------------------------------------- /scripts/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/ILI_script/Client.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 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 | --learning_rate 0.01 \ 25 | --w_lin 0.5 \ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/illness/ \ 32 | --data_path national_illness.csv \ 33 | --model_id ili_36_36 \ 34 | --model $model_name \ 35 | --data custom \ 36 | --features M \ 37 | --seq_len 36 \ 38 | --label_len 18 \ 39 | --pred_len 36 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --learning_rate 0.01 \ 48 | --w_lin 0.5 \ 49 | --itr 1 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/illness/ \ 55 | --data_path national_illness.csv \ 56 | --model_id ili_36_48 \ 57 | --model $model_name \ 58 | --data custom \ 59 | --features M \ 60 | --seq_len 36 \ 61 | --label_len 18 \ 62 | --pred_len 48 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --learning_rate 0.01 \ 71 | --w_lin 0.5 \ 72 | --itr 1 73 | 74 | python -u run.py \ 75 | --task_name long_term_forecast \ 76 | --is_training 1 \ 77 | --root_path ./dataset/illness/ \ 78 | --data_path national_illness.csv \ 79 | --model_id ili_36_60 \ 80 | --model $model_name \ 81 | --data custom \ 82 | --features M \ 83 | --seq_len 36 \ 84 | --label_len 18 \ 85 | --pred_len 60 \ 86 | --e_layers 2 \ 87 | --d_layers 1 \ 88 | --factor 3 \ 89 | --enc_in 7 \ 90 | --dec_in 7 \ 91 | --c_out 7 \ 92 | --des 'Exp' \ 93 | --learning_rate 0.01 \ 94 | --w_lin 0.5 \ 95 | --itr 1 96 | -------------------------------------------------------------------------------- /scripts/ILI_script/Crossformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 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/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 | --d_model 768 \ 24 | --d_ff 768 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --dropout 0.6 \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/illness/ \ 34 | --data_path national_illness.csv \ 35 | --model_id ili_36_36 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len 36 \ 40 | --label_len 18 \ 41 | --pred_len 36 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 7 \ 46 | --dec_in 7 \ 47 | --c_out 7 \ 48 | --d_model 768 \ 49 | --d_ff 768 \ 50 | --top_k 5 \ 51 | --des 'Exp' \ 52 | -dropout 0.6 \ 53 | --itr 1 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/illness/ \ 59 | --data_path national_illness.csv \ 60 | --model_id ili_36_48 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 36 \ 65 | --label_len 18 \ 66 | --pred_len 48 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 7 \ 71 | --dec_in 7 \ 72 | --c_out 7 \ 73 | --d_model 768 \ 74 | --d_ff 768 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | -dropout 0.6 \ 78 | --itr 1 79 | 80 | python -u run.py \ 81 | --task_name long_term_forecast \ 82 | --is_training 1 \ 83 | --root_path ./dataset/illness/ \ 84 | --data_path national_illness.csv \ 85 | --model_id ili_36_60 \ 86 | --model $model_name \ 87 | --data custom \ 88 | --features M \ 89 | --seq_len 36 \ 90 | --label_len 18 \ 91 | --pred_len 60 \ 92 | --e_layers 2 \ 93 | --d_layers 1 \ 94 | --factor 3 \ 95 | --enc_in 7 \ 96 | --dec_in 7 \ 97 | --c_out 7 \ 98 | --d_model 768 \ 99 | --d_ff 768 \ 100 | --top_k 5 \ 101 | --des 'Exp' \ 102 | -dropout 0.6 \ 103 | --itr 1 -------------------------------------------------------------------------------- /scripts/ILI_script/MICN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=4 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/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 36 \ 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 | --d_model 768 \ 24 | --d_ff 768 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --conv_kernel 18 12 \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/illness/ \ 34 | --data_path national_illness.csv \ 35 | --model_id ili_36_36 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len 36 \ 40 | --label_len 36 \ 41 | --pred_len 36 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 7 \ 46 | --dec_in 7 \ 47 | --c_out 7 \ 48 | --d_model 768 \ 49 | --d_ff 768 \ 50 | --top_k 5 \ 51 | --des 'Exp' \ 52 | --conv_kernel 18 12 \ 53 | --itr 1 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/illness/ \ 59 | --data_path national_illness.csv \ 60 | --model_id ili_36_48 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 36 \ 65 | --label_len 36 \ 66 | --pred_len 48 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 7 \ 71 | --dec_in 7 \ 72 | --c_out 7 \ 73 | --d_model 768 \ 74 | --d_ff 768 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | --conv_kernel 18 12 \ 78 | --itr 1 79 | 80 | python -u run.py \ 81 | --task_name long_term_forecast \ 82 | --is_training 1 \ 83 | --root_path ./dataset/illness/ \ 84 | --data_path national_illness.csv \ 85 | --model_id ili_36_60 \ 86 | --model $model_name \ 87 | --data custom \ 88 | --features M \ 89 | --seq_len 36 \ 90 | --label_len 36 \ 91 | --pred_len 60 \ 92 | --e_layers 2 \ 93 | --d_layers 1 \ 94 | --factor 3 \ 95 | --enc_in 7 \ 96 | --dec_in 7 \ 97 | --c_out 7 \ 98 | --d_model 768 \ 99 | --d_ff 768 \ 100 | --top_k 5 \ 101 | --des 'Exp' \ 102 | --conv_kernel 18 12 \ 103 | --itr 1 -------------------------------------------------------------------------------- /scripts/ILI_script/Nonstationary_Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=3 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 32 32 \ 26 | --p_hidden_layers 2 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/illness/ \ 32 | --data_path national_illness.csv \ 33 | --model_id ili_36_36 \ 34 | --model $model_name \ 35 | --data custom \ 36 | --features M \ 37 | --seq_len 36 \ 38 | --label_len 18 \ 39 | --pred_len 36 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --itr 1 \ 48 | --p_hidden_dims 32 32 \ 49 | --p_hidden_layers 2 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/illness/ \ 55 | --data_path national_illness.csv \ 56 | --model_id ili_36_48 \ 57 | --model $model_name \ 58 | --data custom \ 59 | --features M \ 60 | --seq_len 36 \ 61 | --label_len 18 \ 62 | --pred_len 48 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --itr 1 \ 71 | --p_hidden_dims 16 16 \ 72 | --p_hidden_layers 2 73 | 74 | python -u run.py \ 75 | --task_name long_term_forecast \ 76 | --is_training 1 \ 77 | --root_path ./dataset/illness/ \ 78 | --data_path national_illness.csv \ 79 | --model_id ili_36_60 \ 80 | --model $model_name \ 81 | --data custom \ 82 | --features M \ 83 | --seq_len 36 \ 84 | --label_len 18 \ 85 | --pred_len 60 \ 86 | --e_layers 2 \ 87 | --d_layers 1 \ 88 | --factor 3 \ 89 | --enc_in 7 \ 90 | --dec_in 7 \ 91 | --c_out 7 \ 92 | --des 'Exp' \ 93 | --itr 1 \ 94 | --p_hidden_dims 8 8 \ 95 | --p_hidden_layers 2 -------------------------------------------------------------------------------- /scripts/ILI_script/PatchTST.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/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 4 \ 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 | --d_model 1024\ 26 | --itr 1 27 | 28 | python -u run.py \ 29 | --task_name long_term_forecast \ 30 | --is_training 1 \ 31 | --root_path ./dataset/illness/ \ 32 | --data_path national_illness.csv \ 33 | --model_id ili_36_36 \ 34 | --model $model_name \ 35 | --data custom \ 36 | --features M \ 37 | --seq_len 36 \ 38 | --label_len 18 \ 39 | --pred_len 36 \ 40 | --e_layers 4 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 7 \ 44 | --dec_in 7 \ 45 | --c_out 7 \ 46 | --des 'Exp' \ 47 | --n_heads 4 \ 48 | --d_model 2048\ 49 | --itr 1 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/illness/ \ 55 | --data_path national_illness.csv \ 56 | --model_id ili_36_48 \ 57 | --model $model_name \ 58 | --data custom \ 59 | --features M \ 60 | --seq_len 36 \ 61 | --label_len 18 \ 62 | --pred_len 48 \ 63 | --e_layers 4 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 7 \ 67 | --dec_in 7 \ 68 | --c_out 7 \ 69 | --des 'Exp' \ 70 | --n_heads 4 \ 71 | --d_model 2048\ 72 | --itr 1 73 | 74 | 75 | python -u run.py \ 76 | --task_name long_term_forecast \ 77 | --is_training 1 \ 78 | --root_path ./dataset/illness/ \ 79 | --data_path national_illness.csv \ 80 | --model_id ili_36_60 \ 81 | --model $model_name \ 82 | --data custom \ 83 | --features M \ 84 | --seq_len 36 \ 85 | --label_len 18 \ 86 | --pred_len 60 \ 87 | --e_layers 4 \ 88 | --d_layers 1 \ 89 | --factor 3 \ 90 | --enc_in 7 \ 91 | --dec_in 7 \ 92 | --c_out 7 \ 93 | --des 'Exp' \ 94 | --n_heads 16 \ 95 | --d_model 2048\ 96 | --itr 1 -------------------------------------------------------------------------------- /scripts/ILI_script/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimesNet 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 | --d_model 768 \ 24 | --d_ff 768 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/illness/ \ 33 | --data_path national_illness.csv \ 34 | --model_id ili_36_36 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 36 \ 39 | --label_len 18 \ 40 | --pred_len 36 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 7 \ 45 | --dec_in 7 \ 46 | --c_out 7 \ 47 | --d_model 768 \ 48 | --d_ff 768 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/illness/ \ 57 | --data_path national_illness.csv \ 58 | --model_id ili_36_48 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 36 \ 63 | --label_len 18 \ 64 | --pred_len 48 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 7 \ 69 | --dec_in 7 \ 70 | --c_out 7 \ 71 | --d_model 768 \ 72 | --d_ff 768 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/illness/ \ 81 | --data_path national_illness.csv \ 82 | --model_id ili_36_60 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features M \ 86 | --seq_len 36 \ 87 | --label_len 18 \ 88 | --pred_len 60 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 7 \ 93 | --dec_in 7 \ 94 | --c_out 7 \ 95 | --d_model 768 \ 96 | --d_ff 768 \ 97 | --top_k 5 \ 98 | --des 'Exp' \ 99 | --itr 1 100 | -------------------------------------------------------------------------------- /scripts/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/Traffic_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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 92 | -------------------------------------------------------------------------------- /scripts/Traffic_script/Client.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 4 | seq_length=96 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_96_96 \ 12 | --model $model_name \ 13 | --data custom \ 14 | --features M \ 15 | --seq_len $seq_length \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --factor 3 \ 21 | --enc_in 862 \ 22 | --dec_in 862 \ 23 | --c_out 862 \ 24 | --d_model 512 \ 25 | --d_ff 512 \ 26 | --des 'Exp' \ 27 | --learning_rate 0.001 \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/traffic/ \ 34 | --data_path traffic.csv \ 35 | --model_id traffic_96_192 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len $seq_length \ 40 | --label_len 48 \ 41 | --pred_len 192 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 862 \ 46 | --dec_in 862 \ 47 | --c_out 862 \ 48 | --d_model 512 \ 49 | --d_ff 512 \ 50 | --des 'Exp' \ 51 | --learning_rate 0.001 \ 52 | --itr 1 53 | 54 | python -u run.py \ 55 | --task_name long_term_forecast \ 56 | --is_training 1 \ 57 | --root_path ./dataset/traffic/ \ 58 | --data_path traffic.csv \ 59 | --model_id traffic_96_336 \ 60 | --model $model_name \ 61 | --data custom \ 62 | --features M \ 63 | --seq_len $seq_length \ 64 | --label_len 48 \ 65 | --pred_len 336 \ 66 | --e_layers 2 \ 67 | --d_layers 1 \ 68 | --factor 3 \ 69 | --enc_in 862 \ 70 | --dec_in 862 \ 71 | --c_out 862 \ 72 | --d_model 512 \ 73 | --d_ff 512 \ 74 | --des 'Exp' \ 75 | --learning_rate 0.001 \ 76 | --itr 1 77 | 78 | python -u run.py \ 79 | --task_name long_term_forecast \ 80 | --is_training 1 \ 81 | --root_path ./dataset/traffic/ \ 82 | --data_path traffic.csv \ 83 | --model_id traffic_96_720 \ 84 | --model $model_name \ 85 | --data custom \ 86 | --features M \ 87 | --seq_len $seq_length \ 88 | --label_len 48 \ 89 | --pred_len 720 \ 90 | --e_layers 2 \ 91 | --d_layers 1 \ 92 | --factor 3 \ 93 | --enc_in 862 \ 94 | --dec_in 862 \ 95 | --c_out 862 \ 96 | --d_model 512 \ 97 | --d_ff 512 \ 98 | --des 'Exp' \ 99 | --learning_rate 0.001 \ 100 | --itr 1 101 | -------------------------------------------------------------------------------- /scripts/Traffic_script/Crossformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 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/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 96 \ 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 | --top_k 5 \ 24 | --des 'Exp' \ 25 | --n_heads 2 \ 26 | --batch_size 4 \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/traffic/ \ 33 | --data_path traffic.csv \ 34 | --model_id traffic_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 96 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 862 \ 45 | --dec_in 862 \ 46 | --c_out 862 \ 47 | --top_k 5 \ 48 | --des 'Exp' \ 49 | --n_heads 2 \ 50 | --batch_size 4 \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/traffic/ \ 57 | --data_path traffic.csv \ 58 | --model_id traffic_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 96 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 862 \ 69 | --dec_in 862 \ 70 | --c_out 862 \ 71 | --top_k 5 \ 72 | --des 'Exp' \ 73 | --n_heads 2 \ 74 | --batch_size 4 \ 75 | --itr 1 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/traffic/ \ 81 | --data_path traffic.csv \ 82 | --model_id traffic_96_720 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 96 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 862 \ 93 | --dec_in 862 \ 94 | --c_out 862 \ 95 | --top_k 5 \ 96 | --des 'Exp' \ 97 | --n_heads 2 \ 98 | --batch_size 4 \ 99 | --itr 1 -------------------------------------------------------------------------------- /scripts/Traffic_script/MICN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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/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 96 \ 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 | --d_model 512 \ 24 | --d_ff 512 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/traffic/ \ 33 | --data_path traffic.csv \ 34 | --model_id traffic_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 96 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 862 \ 45 | --dec_in 862 \ 46 | --c_out 862 \ 47 | --d_model 512 \ 48 | --d_ff 512 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/traffic/ \ 57 | --data_path traffic.csv \ 58 | --model_id traffic_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 96 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 862 \ 69 | --dec_in 862 \ 70 | --c_out 862 \ 71 | --d_model 512 \ 72 | --d_ff 512 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/traffic/ \ 81 | --data_path traffic.csv \ 82 | --model_id traffic_96_720 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 96 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 862 \ 93 | --dec_in 862 \ 94 | --c_out 862 \ 95 | --d_model 512 \ 96 | --d_ff 512 \ 97 | --top_k 5 \ 98 | --des 'Exp' \ 99 | --itr 1 -------------------------------------------------------------------------------- /scripts/Traffic_script/Nonstationary_Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 128 128 \ 27 | --p_hidden_layers 2 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/traffic/ \ 33 | --data_path traffic.csv \ 34 | --model_id traffic_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 862 \ 45 | --dec_in 862 \ 46 | --c_out 862 \ 47 | --des 'Exp' \ 48 | --itr 1 \ 49 | --train_epochs 3 \ 50 | --p_hidden_dims 128 128 \ 51 | --p_hidden_layers 2 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/traffic/ \ 57 | --data_path traffic.csv \ 58 | --model_id traffic_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 862 \ 69 | --dec_in 862 \ 70 | --c_out 862 \ 71 | --des 'Exp' \ 72 | --itr 1 \ 73 | --train_epochs 3 \ 74 | --p_hidden_dims 16 16 \ 75 | --p_hidden_layers 2 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/traffic/ \ 81 | --data_path traffic.csv \ 82 | --model_id traffic_96_720 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 48 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 862 \ 93 | --dec_in 862 \ 94 | --c_out 862 \ 95 | --des 'Exp' \ 96 | --itr 1 \ 97 | --train_epochs 3 \ 98 | --p_hidden_dims 128 128 \ 99 | --p_hidden_layers 2 -------------------------------------------------------------------------------- /scripts/Traffic_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/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 | --d_model 512 \ 24 | --d_ff 512 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --batch_size 4 \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/traffic/ \ 34 | --data_path traffic.csv \ 35 | --model_id traffic_96_192 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len 96 \ 40 | --label_len 48 \ 41 | --pred_len 192 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 862 \ 46 | --dec_in 862 \ 47 | --c_out 862 \ 48 | --d_model 512 \ 49 | --d_ff 512 \ 50 | --top_k 5 \ 51 | --des 'Exp' \ 52 | --batch_size 4 \ 53 | --itr 1 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/traffic/ \ 59 | --data_path traffic.csv \ 60 | --model_id traffic_96_336 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 48 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 862 \ 71 | --dec_in 862 \ 72 | --c_out 862 \ 73 | --d_model 512 \ 74 | --d_ff 512 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | --batch_size 4 \ 78 | --itr 1 79 | 80 | python -u run.py \ 81 | --task_name long_term_forecast \ 82 | --is_training 1 \ 83 | --root_path ./dataset/traffic/ \ 84 | --data_path traffic.csv \ 85 | --model_id traffic_96_720 \ 86 | --model $model_name \ 87 | --data custom \ 88 | --features M \ 89 | --seq_len 96 \ 90 | --label_len 48 \ 91 | --pred_len 720 \ 92 | --e_layers 2 \ 93 | --d_layers 1 \ 94 | --factor 3 \ 95 | --enc_in 862 \ 96 | --dec_in 862 \ 97 | --c_out 862 \ 98 | --d_model 512 \ 99 | --d_ff 512 \ 100 | --top_k 5 \ 101 | --des 'Exp' \ 102 | --batch_size 4 \ 103 | --itr 1 -------------------------------------------------------------------------------- /scripts/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/Traffic_script/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimesNet 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 | --d_model 512 \ 24 | --d_ff 512 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/traffic/ \ 33 | --data_path traffic.csv \ 34 | --model_id traffic_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 862 \ 45 | --dec_in 862 \ 46 | --c_out 862 \ 47 | --d_model 512 \ 48 | --d_ff 512 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 52 | 53 | python -u run.py \ 54 | --task_name long_term_forecast \ 55 | --is_training 1 \ 56 | --root_path ./dataset/traffic/ \ 57 | --data_path traffic.csv \ 58 | --model_id traffic_96_336 \ 59 | --model $model_name \ 60 | --data custom \ 61 | --features M \ 62 | --seq_len 96 \ 63 | --label_len 48 \ 64 | --pred_len 336 \ 65 | --e_layers 2 \ 66 | --d_layers 1 \ 67 | --factor 3 \ 68 | --enc_in 862 \ 69 | --dec_in 862 \ 70 | --c_out 862 \ 71 | --d_model 512 \ 72 | --d_ff 512 \ 73 | --top_k 5 \ 74 | --des 'Exp' \ 75 | --itr 1 76 | 77 | python -u run.py \ 78 | --task_name long_term_forecast \ 79 | --is_training 1 \ 80 | --root_path ./dataset/traffic/ \ 81 | --data_path traffic.csv \ 82 | --model_id traffic_96_720 \ 83 | --model $model_name \ 84 | --data custom \ 85 | --features M \ 86 | --seq_len 96 \ 87 | --label_len 48 \ 88 | --pred_len 720 \ 89 | --e_layers 2 \ 90 | --d_layers 1 \ 91 | --factor 3 \ 92 | --enc_in 862 \ 93 | --dec_in 862 \ 94 | --c_out 862 \ 95 | --d_model 512 \ 96 | --d_ff 512 \ 97 | --top_k 5 \ 98 | --des 'Exp' \ 99 | --itr 1 -------------------------------------------------------------------------------- /scripts/Traffic_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/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 | --d_model 512 \ 24 | --d_ff 512 \ 25 | --des 'Exp' \ 26 | --itr 1 \ 27 | --train_epochs 3 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/traffic/ \ 33 | --data_path traffic.csv \ 34 | --model_id traffic_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 1 \ 45 | --dec_in 1 \ 46 | --c_out 1 \ 47 | --des 'Exp' \ 48 | --itr 1 \ 49 | --train_epochs 3 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/traffic/ \ 55 | --data_path traffic.csv \ 56 | --model_id traffic_96_336 \ 57 | --model $model_name \ 58 | --data custom \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 1 \ 67 | --dec_in 1 \ 68 | --c_out 1 \ 69 | --des 'Exp' \ 70 | --itr 1 \ 71 | --train_epochs 3 72 | 73 | python -u run.py \ 74 | --task_name long_term_forecast \ 75 | --is_training 1 \ 76 | --root_path ./dataset/traffic/ \ 77 | --data_path traffic.csv \ 78 | --model_id traffic_96_720 \ 79 | --model $model_name \ 80 | --data custom \ 81 | --features M \ 82 | --seq_len 96 \ 83 | --label_len 48 \ 84 | --pred_len 720 \ 85 | --e_layers 2 \ 86 | --d_layers 1 \ 87 | --factor 3 \ 88 | --enc_in 1 \ 89 | --dec_in 1 \ 90 | --c_out 1 \ 91 | --des 'Exp' \ 92 | --itr 1 \ 93 | --train_epochs 3 94 | -------------------------------------------------------------------------------- /scripts/Weather_script/Autoformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 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 89 | -------------------------------------------------------------------------------- /scripts/Weather_script/Client.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=Client 4 | seq_length=96 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_96_96 \ 12 | --model $model_name \ 13 | --data custom \ 14 | --features M \ 15 | --seq_len $seq_length \ 16 | --label_len 48 \ 17 | --pred_len 96 \ 18 | --e_layers 2 \ 19 | --d_layers 1 \ 20 | --factor 3 \ 21 | --enc_in 21 \ 22 | --dec_in 21 \ 23 | --c_out 21 \ 24 | --d_model 32 \ 25 | --d_ff 32 \ 26 | --learning_rate 0.001\ 27 | --des 'Exp' \ 28 | --itr 1 29 | 30 | python -u run.py \ 31 | --task_name long_term_forecast \ 32 | --is_training 1 \ 33 | --root_path ./dataset/weather/ \ 34 | --data_path weather.csv \ 35 | --model_id weather_96_192 \ 36 | --model $model_name \ 37 | --data custom \ 38 | --features M \ 39 | --seq_len $seq_length \ 40 | --label_len 48 \ 41 | --pred_len 192 \ 42 | --e_layers 2 \ 43 | --d_layers 1 \ 44 | --factor 3 \ 45 | --enc_in 21 \ 46 | --dec_in 21 \ 47 | --c_out 21 \ 48 | --d_model 32 \ 49 | --d_ff 32 \ 50 | --des 'Exp' \ 51 | --learning_rate 0.001\ 52 | --itr 1 \ 53 | --train_epochs 10 54 | 55 | 56 | python -u run.py \ 57 | --task_name long_term_forecast \ 58 | --is_training 1 \ 59 | --root_path ./dataset/weather/ \ 60 | --data_path weather.csv \ 61 | --model_id weather_96_336 \ 62 | --model $model_name \ 63 | --data custom \ 64 | --features M \ 65 | --seq_len $seq_length \ 66 | --label_len 48 \ 67 | --pred_len 336 \ 68 | --e_layers 2 \ 69 | --d_layers 1 \ 70 | --factor 3 \ 71 | --enc_in 21 \ 72 | --dec_in 21 \ 73 | --c_out 21 \ 74 | --d_model 32 \ 75 | --d_ff 32 \ 76 | --des 'Exp' \ 77 | --learning_rate 0.001\ 78 | --itr 1 79 | 80 | python -u run.py \ 81 | --task_name long_term_forecast \ 82 | --is_training 1 \ 83 | --root_path ./dataset/weather/ \ 84 | --data_path weather.csv \ 85 | --model_id weather_96_720 \ 86 | --model $model_name \ 87 | --data custom \ 88 | --features M \ 89 | --seq_len $seq_length \ 90 | --label_len 48 \ 91 | --pred_len 720 \ 92 | --e_layers 2 \ 93 | --d_layers 1 \ 94 | --factor 3 \ 95 | --enc_in 21 \ 96 | --dec_in 21 \ 97 | --c_out 21 \ 98 | --d_model 32 \ 99 | --d_ff 32 \ 100 | --des 'Exp' \ 101 | --itr 1 \ 102 | --learning_rate 0.001\ 103 | --train_epochs 10 104 | -------------------------------------------------------------------------------- /scripts/Weather_script/Crossformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=7 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/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 | --d_model 32 \ 24 | --d_ff 32 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/weather/ \ 33 | --data_path weather.csv \ 34 | --model_id weather_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 21 \ 45 | --dec_in 21 \ 46 | --c_out 21 \ 47 | --d_model 32 \ 48 | --d_ff 32 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 \ 52 | --train_epochs 1 53 | 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/weather/ \ 59 | --data_path weather.csv \ 60 | --model_id weather_96_336 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 48 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 21 \ 71 | --dec_in 21 \ 72 | --c_out 21 \ 73 | --d_model 32 \ 74 | --d_ff 32 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | --itr 1 78 | 79 | python -u run.py \ 80 | --task_name long_term_forecast \ 81 | --is_training 1 \ 82 | --root_path ./dataset/weather/ \ 83 | --data_path weather.csv \ 84 | --model_id weather_96_720 \ 85 | --model $model_name \ 86 | --data custom \ 87 | --features M \ 88 | --seq_len 96 \ 89 | --label_len 48 \ 90 | --pred_len 720 \ 91 | --e_layers 2 \ 92 | --d_layers 1 \ 93 | --factor 3 \ 94 | --enc_in 21 \ 95 | --dec_in 21 \ 96 | --c_out 21 \ 97 | --d_model 32 \ 98 | --d_ff 32 \ 99 | --top_k 5 \ 100 | --des 'Exp' \ 101 | --itr 1 \ 102 | --train_epochs 1 -------------------------------------------------------------------------------- /scripts/Weather_script/MICN.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=5 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/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 96 \ 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 | --d_model 32 \ 24 | --d_ff 32 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/weather/ \ 33 | --data_path weather.csv \ 34 | --model_id weather_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 96 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 21 \ 45 | --dec_in 21 \ 46 | --c_out 21 \ 47 | --d_model 32 \ 48 | --d_ff 32 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 \ 52 | --train_epochs 1 53 | 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/weather/ \ 59 | --data_path weather.csv \ 60 | --model_id weather_96_336 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 96 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 21 \ 71 | --dec_in 21 \ 72 | --c_out 21 \ 73 | --d_model 32 \ 74 | --d_ff 32 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | --itr 1 78 | 79 | python -u run.py \ 80 | --task_name long_term_forecast \ 81 | --is_training 1 \ 82 | --root_path ./dataset/weather/ \ 83 | --data_path weather.csv \ 84 | --model_id weather_96_720 \ 85 | --model $model_name \ 86 | --data custom \ 87 | --features M \ 88 | --seq_len 96 \ 89 | --label_len 96 \ 90 | --pred_len 720 \ 91 | --e_layers 2 \ 92 | --d_layers 1 \ 93 | --factor 3 \ 94 | --enc_in 21 \ 95 | --dec_in 21 \ 96 | --c_out 21 \ 97 | --d_model 32 \ 98 | --d_ff 32 \ 99 | --top_k 5 \ 100 | --des 'Exp' \ 101 | --itr 1 \ 102 | --train_epochs 1 -------------------------------------------------------------------------------- /scripts/Weather_script/Nonstationary_Transformer.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=6 2 | 3 | model_name=Nonstationary_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 | --p_hidden_dims 256 256 \ 27 | --p_hidden_layers 2 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/weather/ \ 33 | --data_path weather.csv \ 34 | --model_id weather_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 21 \ 45 | --dec_in 21 \ 46 | --c_out 21 \ 47 | --des 'Exp' \ 48 | --itr 1 \ 49 | --p_hidden_dims 128 128 \ 50 | --p_hidden_layers 2 51 | 52 | python -u run.py \ 53 | --task_name long_term_forecast \ 54 | --is_training 1 \ 55 | --root_path ./dataset/weather/ \ 56 | --data_path weather.csv \ 57 | --model_id weather_96_336 \ 58 | --model $model_name \ 59 | --data custom \ 60 | --features M \ 61 | --seq_len 96 \ 62 | --label_len 48 \ 63 | --pred_len 336 \ 64 | --e_layers 2 \ 65 | --d_layers 1 \ 66 | --factor 3 \ 67 | --enc_in 21 \ 68 | --dec_in 21 \ 69 | --c_out 21 \ 70 | --des 'Exp' \ 71 | --itr 1 \ 72 | --p_hidden_dims 128 128 \ 73 | --p_hidden_layers 2 74 | 75 | python -u run.py \ 76 | --task_name long_term_forecast \ 77 | --is_training 1 \ 78 | --root_path ./dataset/weather/ \ 79 | --data_path weather.csv \ 80 | --model_id weather_96_720 \ 81 | --model $model_name \ 82 | --data custom \ 83 | --features M \ 84 | --seq_len 96 \ 85 | --label_len 48 \ 86 | --pred_len 720 \ 87 | --e_layers 2 \ 88 | --d_layers 1 \ 89 | --factor 3 \ 90 | --enc_in 21 \ 91 | --dec_in 21 \ 92 | --c_out 21 \ 93 | --des 'Exp' \ 94 | --itr 1 \ 95 | --p_hidden_dims 128 128 \ 96 | --p_hidden_layers 2 -------------------------------------------------------------------------------- /scripts/Weather_script/PatchTST.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/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 | --n_heads 4 \ 26 | --train_epochs 3 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 M \ 37 | --seq_len 96 \ 38 | --label_len 48 \ 39 | --pred_len 192 \ 40 | --e_layers 2 \ 41 | --d_layers 1 \ 42 | --factor 3 \ 43 | --enc_in 21 \ 44 | --dec_in 21 \ 45 | --c_out 21 \ 46 | --des 'Exp' \ 47 | --itr 1 \ 48 | --n_heads 16 \ 49 | --train_epochs 3 50 | 51 | python -u run.py \ 52 | --task_name long_term_forecast \ 53 | --is_training 1 \ 54 | --root_path ./dataset/weather/ \ 55 | --data_path weather.csv \ 56 | --model_id weather_96_336 \ 57 | --model $model_name \ 58 | --data custom \ 59 | --features M \ 60 | --seq_len 96 \ 61 | --label_len 48 \ 62 | --pred_len 336 \ 63 | --e_layers 2 \ 64 | --d_layers 1 \ 65 | --factor 3 \ 66 | --enc_in 21 \ 67 | --dec_in 21 \ 68 | --c_out 21 \ 69 | --des 'Exp' \ 70 | --itr 1 \ 71 | --n_heads 4 \ 72 | --batch_size 128 \ 73 | --train_epochs 3 74 | 75 | python -u run.py \ 76 | --task_name long_term_forecast \ 77 | --is_training 1 \ 78 | --root_path ./dataset/weather/ \ 79 | --data_path weather.csv \ 80 | --model_id weather_96_720 \ 81 | --model $model_name \ 82 | --data custom \ 83 | --features M \ 84 | --seq_len 96 \ 85 | --label_len 48 \ 86 | --pred_len 720 \ 87 | --e_layers 2 \ 88 | --d_layers 1 \ 89 | --factor 3 \ 90 | --enc_in 21 \ 91 | --dec_in 21 \ 92 | --c_out 21 \ 93 | --des 'Exp' \ 94 | --itr 1 \ 95 | --n_heads 4 \ 96 | --batch_size 128 \ 97 | --train_epochs 3 -------------------------------------------------------------------------------- /scripts/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/Weather_script/TimesNet.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=0 2 | 3 | model_name=TimesNet 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 | --d_model 32 \ 24 | --d_ff 32 \ 25 | --top_k 5 \ 26 | --des 'Exp' \ 27 | --itr 1 28 | 29 | python -u run.py \ 30 | --task_name long_term_forecast \ 31 | --is_training 1 \ 32 | --root_path ./dataset/weather/ \ 33 | --data_path weather.csv \ 34 | --model_id weather_96_192 \ 35 | --model $model_name \ 36 | --data custom \ 37 | --features M \ 38 | --seq_len 96 \ 39 | --label_len 48 \ 40 | --pred_len 192 \ 41 | --e_layers 2 \ 42 | --d_layers 1 \ 43 | --factor 3 \ 44 | --enc_in 21 \ 45 | --dec_in 21 \ 46 | --c_out 21 \ 47 | --d_model 32 \ 48 | --d_ff 32 \ 49 | --top_k 5 \ 50 | --des 'Exp' \ 51 | --itr 1 \ 52 | --train_epochs 1 53 | 54 | 55 | python -u run.py \ 56 | --task_name long_term_forecast \ 57 | --is_training 1 \ 58 | --root_path ./dataset/weather/ \ 59 | --data_path weather.csv \ 60 | --model_id weather_96_336 \ 61 | --model $model_name \ 62 | --data custom \ 63 | --features M \ 64 | --seq_len 96 \ 65 | --label_len 48 \ 66 | --pred_len 336 \ 67 | --e_layers 2 \ 68 | --d_layers 1 \ 69 | --factor 3 \ 70 | --enc_in 21 \ 71 | --dec_in 21 \ 72 | --c_out 21 \ 73 | --d_model 32 \ 74 | --d_ff 32 \ 75 | --top_k 5 \ 76 | --des 'Exp' \ 77 | --itr 1 78 | 79 | python -u run.py \ 80 | --task_name long_term_forecast \ 81 | --is_training 1 \ 82 | --root_path ./dataset/weather/ \ 83 | --data_path weather.csv \ 84 | --model_id weather_96_720 \ 85 | --model $model_name \ 86 | --data custom \ 87 | --features M \ 88 | --seq_len 96 \ 89 | --label_len 48 \ 90 | --pred_len 720 \ 91 | --e_layers 2 \ 92 | --d_layers 1 \ 93 | --factor 3 \ 94 | --enc_in 21 \ 95 | --dec_in 21 \ 96 | --c_out 21 \ 97 | --d_model 32 \ 98 | --d_ff 32 \ 99 | --top_k 5 \ 100 | --des 'Exp' \ 101 | --itr 1 \ 102 | --train_epochs 1 103 | -------------------------------------------------------------------------------- /scripts/Weather_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/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 89 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/daxin007/Client/802c603f5ff3dd7814afd3e096f9082873373f1e/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(pred - true)) 16 | 17 | 18 | def MSE(pred, true): 19 | return np.mean((pred - true) ** 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((pred - true) / true)) 28 | 29 | 30 | def MSPE(pred, true): 31 | return np.mean(np.square((pred - true) / 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 | --------------------------------------------------------------------------------