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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Sundial 2 | 3 | This is the official repository of [Sundial: A Family of Highly Capable Time Series Foundation Models](https://arxiv.org/abs/2502.00]816). 4 | 5 |

6 | 7 |

8 | 9 | ## Updates 10 | 11 | :triangular_flag_on_post: **News** (2025.06) Sundial has been accepted as **ICML 2025 Oral** (Top 1%). See you at Vancouver :) 12 | 13 | :triangular_flag_on_post: **News** (2025.05) Get **1st MASE** on the [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) Benchmark. 14 | 15 | :triangular_flag_on_post: **News** (2025.05) Released a **trillion-scale** pre-trained model on [HuggingFace](https://huggingface.co/thuml/sundial-base-128m). A quickstart is provided [here](./examples/quickstart_zero_shot.ipynb). 16 | 17 | :triangular_flag_on_post: **News** (2025.02) Get **1st MSE/MAE** zero-shot performance on [Time-Series-Library](https://github.com/thuml/Time-Series-Library) datasets. 18 | 19 | ## Introduction 20 | 21 | Sundial is a family of **generative** time series foundation models, which is pre-trained on TimeBench (**10^12** time points). The model can be applied for both **point** and **probabilistic** forecasting. 22 | 23 | Not only the mean or quantiles, you can estimate anything about the predictive distribution with raw generated samples. 24 | 25 | We propose **TimeFlow Loss** to predict next-patch’s distribution, allowing Transformers to be trained **without discrete tokenization** and make **non-deterministic predictions**. 26 | 27 |

28 | 29 |

30 | 31 | ## Quickstart 32 | 33 | We release a [HuggingFace model](https://huggingface.co/thuml/sundial-base-128m), which can make zero-shot predictions on CPU within seconds! 🚀 34 | 35 | > Inference Time on Apple M1 Pro CPU (16 GB) 36 | 37 | | Lookback | Forcast | # Generated | Wall-Clock Time | Accelerate By | 38 | | --------------- | ----------------- | ------------------- | -------------- | -------------- | 39 | | 672 | 16 | 1 | 249ms | - | 40 | | 2880 | 16 | 1 | 510ms | FlashAttention | 41 | | 2880 | 720 | 1 | 510ms | Multi-Patch Prediction | 42 | | 2880 | 1440 | 1 | 789ms | KV Cache | 43 | | 2880 | 720 | 20 | 949ms | Shared Condition | 44 | 45 | All you need is a network connection and the HuggingFace access! 46 | 47 | ``` 48 | pip install transformers==4.40.1 49 | ``` 50 | 51 | ``` 52 | import torch 53 | from transformers import AutoModelForCausalLM 54 | 55 | # load pretrain model 56 | # supports different lookback/forecast lengths 57 | model = AutoModelForCausalLM.from_pretrained('thuml/sundial-base-128m', trust_remote_code=True) 58 | 59 | # prepare input 60 | batch_size, lookback_length = 1, 2880 61 | seqs = torch.randn(batch_size, lookback_length) 62 | 63 | # Note that Sundial can generate multiple probable predictions 64 | forecast_length = 96 65 | num_samples = 20 66 | 67 | output = model.generate(seqs, max_new_tokens=forecast_length, num_samples=num_samples) 68 | 69 | # use raw predictions for mean/quantiles/confidence-interval estimation 70 | print(output.shape) 71 | ``` 72 | 73 | More examples of predicting quantiles or confidence intervals are provided in this [notebook](https://github.com/thuml/Sundial/blob/main/examples/quickstart_zero_shot.ipynb). Please raise your valuable suggestions [here](https://huggingface.co/thuml/sundial-base-128m/discussions/new), we 'd like to solve it ASAP 🤗. 74 | 75 | 76 | 77 | ## Architecture 78 | 79 |

80 | 81 |

82 | 83 | > Intuitively, Sundial can be viewed as an **ARMA** model (Auto-Regression and Moving-Average). Transformer learns auto-regressive token representations. Conditioned on them, TimeFlow transforms random noises into non-deterministic predictions. 84 | 85 | ## Model Configurations 86 | 87 | We have currently built three different sizes of Sundial. Model configurations are provided here: 88 | 89 |

90 | 91 |

92 | 93 | ## Evaluation 94 | 95 | We evaluate Sundial (Base) with advanced time series foundation models on well-recognized benchmarks: 96 | 97 | - [GIFT-Eval (1st MASE)](https://cdn-uploads.huggingface.co/production/uploads/64fbe24a2d20ced4e91de38a/3BxatwayhK5GAoqMf1oHv.png) [[Leaderboard]](https://huggingface.co/spaces/Salesforce/GIFT-Eval). 98 | - [Time-Series-Library (1st MSE/MAE)](https://cdn-uploads.huggingface.co/production/uploads/64fbe24a2d20ced4e91de38a/5VqnFwWTWoYz877Zkluiw.png). 99 | - [FEV Leaderboard](https://cdn-uploads.huggingface.co/production/uploads/64fbe24a2d20ced4e91de38a/mrKL9QmX-aX8rCiwxKgmA.png). 100 | 101 | ## Exciting News ✨ 102 | 103 | Code for fine-tuning is on its way and will be available soon! Stay tuned for updates! 104 | 105 | ## Citation 106 | 107 | If you find this repo helpful, please cite our paper. 108 | 109 | 110 | ``` 111 | @article{liu2025sundial, 112 | title={Sundial: A Family of Highly Capable Time Series Foundation Models}, 113 | author={Liu, Yong and Qin, Guo and Shi, Zhiyuan and Chen, Zhi and Yang, Caiyin and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng}, 114 | journal={arXiv preprint arXiv:2502.00816}, 115 | year={2025} 116 | } 117 | ``` 118 | 119 | ## Acknowledgment 120 | 121 | We appreciate the following resources a lot for their valuable code and datasets: 122 | 123 | - Time-Series-Library (https://github.com/thuml/Time-Series-Library) 124 | - Large-Time-Series-Model & UTSD (https://github.com/thuml/Large-Time-Series-Model) 125 | - Timer-XL (https://github.com/thuml/Timer-XL) 126 | - LoTSA Data (https://huggingface.co/datasets/Salesforce/lotsa_data) 127 | - Chronos Datasets (https://huggingface.co/datasets/autogluon/chronos_datasets) 128 | 129 | ## Contact 130 | 131 | If you have any questions or want to use the code, feel free to contact: 132 | 133 | * Yong Liu (liuyong21@mails.tsinghua.edu.cn) 134 | * Guo Qin (qinguo24@mails.tsinghua.edu.cn) 135 | 136 | ## License 137 | 138 | This model is licensed under the Apache-2.0 License. 139 | -------------------------------------------------------------------------------- /figures/arch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Sundial/06bdbf1bd58760c0ffa69978362c0ef2dd73d3e3/figures/arch.png -------------------------------------------------------------------------------- /figures/compare.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Sundial/06bdbf1bd58760c0ffa69978362c0ef2dd73d3e3/figures/compare.png -------------------------------------------------------------------------------- /figures/config.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Sundial/06bdbf1bd58760c0ffa69978362c0ef2dd73d3e3/figures/config.png -------------------------------------------------------------------------------- /figures/cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thuml/Sundial/06bdbf1bd58760c0ffa69978362c0ef2dd73d3e3/figures/cover.png --------------------------------------------------------------------------------