TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
🚩News (2024.10) We have included [TimeXer], which defined a practical forecasting paradigm: Forecasting with Exogenous Variables. Considering both practicability and computation efficiency, we believe the new forecasting paradigm defined in TimeXer can be the "right" task for future research.
🚩News (2024.10) Our lab has open-sourced [OpenLTM], which provides a distinct pretrain-finetuning paradigm compared to TSLib. If you are interested in Large Time Series Models, you may find this repository helpful.
🚩News (2024.07) We wrote a comprehensive survey of [Deep Time Series Models] with a rigorous benchmark based on TSLib. In this paper, we summarized the design principles of current time series models supported by insightful experiments, hoping to be helpful to future research.
🚩News (2024.04) Many thanks for the great work from frecklebars. The famous sequential model Mamba has been included in our library. See this file, where you need to install mamba_ssm with pip at first.
🚩News (2024.03) Given the inconsistent look-back length of various papers, we split the long-term forecasting in the leaderboard into two categories: Look-Back-96 and Look-Back-Searching. We recommend researchers read TimeMixer, which includes both look-back length settings in experiments for scientific rigor.
🚩News (2023.10) We add an implementation to iTransformer, which is the state-of-the-art model for long-term forecasting. The official code and complete scripts of iTransformer can be found here.
🚩News (2023.09) We added a detailed tutorial for TimesNet and this library, which is quite friendly to beginners of deep time series analysis.
🚩News (2023.02) We release the TSlib as a comprehensive benchmark and code base for time series models, which is extended from our previous GitHub repository Autoformer.
Till March 2024, the top three models for five different tasks are:
| Model Ranking |
Long-term Forecasting Look-Back-96 |
Long-term Forecasting Look-Back-Searching |
Short-term Forecasting |
Imputation | Classification | Anomaly Detection |
|---|---|---|---|---|---|---|
| 🥇 1st | TimeXer | TimeMixer | TimesNet | TimesNet | TimesNet | TimesNet |
| 🥈 2nd | iTransformer | PatchTST | Non-stationary Transformer |
Non-stationary Transformer |
Non-stationary Transformer |
FEDformer |
| 🥉 3rd | TimeMixer | DLinear | FEDformer | Autoformer | Informer | Autoformer |
Note: We will keep updating this leaderboard. If you have proposed advanced and awesome models, you can send us your paper/code link or raise a pull request. We will add them to this repo and update the leaderboard as soon as possible.
Compared models of this leaderboard. ☑ means that their codes have already been included in this repo.
- TimeXer - TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables [NeurIPS 2024] [Code]
- TimeMixer - TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting [ICLR 2024] [Code].
- TSMixer - TSMixer: An All-MLP Architecture for Time Series Forecasting [arXiv 2023] [Code]
- iTransformer - iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [ICLR 2024] [Code].
- PatchTST - A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [ICLR 2023] [Code].
- TimesNet - TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [ICLR 2023] [Code].
- DLinear - Are Transformers Effective for Time Series Forecasting? [AAAI 2023] [Code].
- LightTS - Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures [arXiv 2022] [Code].
- ETSformer - ETSformer: Exponential Smoothing Transformers for Time-series Forecasting [arXiv 2022] [Code].
- Non-stationary Transformer - Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting [NeurIPS 2022] [Code].
- FEDformer - FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [ICML 2022] [Code].
- Pyraformer - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting [ICLR 2022] [Code].
- Autoformer - Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [NeurIPS 2021] [Code].
- Informer - Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [AAAI 2021] [Code].
- Reformer - Reformer: The Efficient Transformer [ICLR 2020] [Code].
- Transformer - Attention is All You Need [NeurIPS 2017] [Code].
See our latest paper [TimesNet] for the comprehensive benchmark. We will release a real-time updated online version soon.
Newly added baselines. We will add them to the leaderboard after a comprehensive evaluation.
- PAttn - Are Language Models Actually Useful for Time Series Forecasting? [NeurIPS 2024] [Code]
- Mamba - Mamba: Linear-Time Sequence Modeling with Selective State Spaces [arXiv 2023] [Code]
- SegRNN - SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting [arXiv 2023] [Code].
- Koopa - Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors [NeurIPS 2023] [Code].
- FreTS - Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [NeurIPS 2023] [Code].
- MICN - MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting [ICLR 2023][Code].
- Crossformer - Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting [ICLR 2023][Code].
- TiDE - Long-term Forecasting with TiDE: Time-series Dense Encoder [arXiv 2023] [Code].
- SCINet - SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [NeurIPS 2022][Code].
- FiLM - FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [NeurIPS 2022][Code].
- TFT - Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting [arXiv 2019][Code].
- Install Python 3.8. For convenience, execute the following command.
pip install -r requirements.txt
- Prepare Data. You can obtain the well pre-processed datasets from [Google Drive] or [Baidu Drive], Then place the downloaded data in the folder
./dataset. Here is a summary of supported datasets.
- Train and evaluate model. We provide the experiment scripts for all benchmarks under the folder
./scripts/. You can reproduce the experiment results as the following examples:
# long-term forecast
bash ./scripts/long_term_forecast/ETT_script/TimesNet_ETTh1.sh
# short-term forecast
bash ./scripts/short_term_forecast/TimesNet_M4.sh
# imputation
bash ./scripts/imputation/ETT_script/TimesNet_ETTh1.sh
# anomaly detection
bash ./scripts/anomaly_detection/PSM/TimesNet.sh
# classification
bash ./scripts/classification/TimesNet.sh
- Develop your own model.
- Add the model file to the folder
./models. You can follow the./models/Transformer.py. - Include the newly added model in the
Exp_Basic.model_dictof./exp/exp_basic.py. - Create the corresponding scripts under the folder
./scripts.
Note:
(1) About classification: Since we include all five tasks in a unified code base, the accuracy of each subtask may fluctuate but the average performance can be reproduced (even a bit better). We have provided the reproduced checkpoints here.
(2) About anomaly detection: Some discussion about the adjustment strategy in anomaly detection can be found here. The key point is that the adjustment strategy corresponds to an event-level metric.
If you find this repo useful, please cite our paper.
@inproceedings{wu2023timesnet,
title={TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis},
author={Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long},
booktitle={International Conference on Learning Representations},
year={2023},
}
@article{wang2024tssurvey,
title={Deep Time Series Models: A Comprehensive Survey and Benchmark},
author={Yuxuan Wang and Haixu Wu and Jiaxiang Dong and Yong Liu and Mingsheng Long and Jianmin Wang},
booktitle={arXiv preprint arXiv:2407.13278},
year={2024},
}
If you have any questions or suggestions, feel free to contact our maintenance team:
Current:
- Haixu Wu (Ph.D. student, wuhx23@mails.tsinghua.edu.cn)
- Yong Liu (Ph.D. student, liuyong21@mails.tsinghua.edu.cn)
- Yuxuan Wang (Ph.D. student, wangyuxu22@mails.tsinghua.edu.cn)
- Huikun Weng (Undergraduate, wenghk22@mails.tsinghua.edu.cn)
Previous:
- Tengge Hu (Master student, htg21@mails.tsinghua.edu.cn)
- Haoran Zhang (Master student, z-hr20@mails.tsinghua.edu.cn)
- Jiawei Guo (Undergraduate, guo-jw21@mails.tsinghua.edu.cn)
Or describe it in Issues.
This project is supported by the National Key R&D Program of China (2021YFB1715200).
This library is constructed based on the following repos:
-
Forecasting: https://github.com/thuml/Autoformer.
-
Anomaly Detection: https://github.com/thuml/Anomaly-Transformer.
-
Classification: https://github.com/thuml/Flowformer.
All the experiment datasets are public, and we obtain them from the following links:
-
Long-term Forecasting and Imputation: https://github.com/thuml/Autoformer.
-
Short-term Forecasting: https://github.com/ServiceNow/N-BEATS.
-
Anomaly Detection: https://github.com/thuml/Anomaly-Transformer.
-
Classification: https://www.timeseriesclassification.com/.
