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 (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.

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

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.

• iTransformer - iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [arXiv 2023] [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] [ICLR 2024] [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.

• Koopa - Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors [NeurIPS 2023] [Code].
• TiDE - Long-term Forecasting with TiDE: Time-series Dense Encoder [arXiv 2023] [Code].
• FiLM - FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [NeurIPS 2022][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].

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

pip install -r requirements.txt

2. 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.

3. 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

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.

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},
}

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

• Haixu Wu ([email protected])
• Tengge Hu ([email protected])
• Haoran Zhang ([email protected])
• Jiawei Guo ([email protected])

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/.