Time Series Library (TSlib)

TSlib is an open-source library for deep learning researchers, especially deep time series analysis.

We provide a neat code base to evaluate advanced deep time series models or develop your own model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.

Leaderboard for Time Series Analysis

Till February 2023, the top three models for five different tasks are:

Model Ranking	Long-term Forecasting	Short-term Forecasting	Imputation	Anomaly Detection	Classification
🥇 1st	TimesNet	TimesNet	TimesNet	TimesNet	TimesNet
🥈 2nd	DLinear	Non-stationary Transformer	Non-stationary Transformer	Non-stationary Transformer	FEDformer
🥉 3rd	Non-stationary Transformer	FEDformer	Autoformer	Informer	Autoformer

Note: We will keep updating this leaderborad. If you have proposed advanced and awesome models, welcome to send your paper/code link to us or raise a pull request. We will add them to this repo and update the leaderborad as soon as possible.

Compared models of this leaderboard. ☑ means that their codes have already been included in this repo.

• 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 in March.

Newly added baselines. We will add them into the leaderboard after a comprehensive evaluation.

• PatchTST - A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. [ICLR 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]

Usage

1. Install Python 3.8. For convenience, execute the following command.

pip install -r requirements.txt

2. Prepare Data. You can obtained the well pre-processed datasets from [Google Drive], [Tsinghua Cloud] or [Baidu Drive]. Then place the downloaded data under the folder ./dataset. Here is a summary of supported datasets.

3. Train and evaluate model. We provide the experiment scripts of 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

4. 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_dict of ./exp/exp_basic.py.
• Create the corresponding scripts under the folder ./scripts.

Citation

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},
}

Contact

If you have any questions or suggestions, feel free to contact:

• Haixu Wu (whx20@mails.tsinghua.edu.cn)
• Tengge Hu (htg21@mails.tsinghua.edu.cn)
• Haoran Zhang (z-hr20@mails.tsinghua.edu.cn)

or describe it in Issues.

Acknowledgement

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/