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.
Till February 2023, the top three models for five different tasks are:
Model Ranking |
Long-term Forecasting |
Short-term Forecasting |
Imputation | Anomaly Detection |
Classification |
---|---|---|---|---|---|
๐ฅ 1st | TimesNet | TimesNet | TimesNet | TimesNet | TimesNet |
๐ฅ 2nd | DLinear | Non-stationary Transformer |
Non-stationary Transformer |
Non-stationary Transformer |
FEDformer |
๐ฅ 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. โ 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 [ICLR2023][Code]
- Install Python 3.8. For convenience, execute the following command.
pip install -r requirements.txt
- 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.
- 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
- 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])
or describe it in Issues.
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/