Time-Series-Library (TSLib)

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.

This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current time-series models and their performance, please refer to our paper Deep Time Series Models: A Comprehensive Survey and Benchmark.

To get started with the codebase and contribute, please visit the GitHub repository.

Dataset Overview

Tasks	Benchmarks	Metrics	Series Length
Forecasting	Long-term: ETT (4 subsets), Electricity, Traffic, Weather, Exchange, ILI	MSE, MAE	96~720 (ILI: 24~60)
	Short-term: M4 (6 subsets)	SMAPE, MASE, OWA	6~48
Imputation	ETT (4 subsets), Electricity, Weather	MSE, MAE	96
Classification	UEA (10 subsets)	Accuracy	29~1751
Anomaly Detection	SMD, MSL, SMAP, SWaT, PSM	Precision, Recall, F1-Score	100

File Structure

Time-Series-Library/
├── ETT-small/
├── EthanolConcentration/
├── FaceDetection/
├── Handwriting/
├── Heartbeat/
├── JapaneseVowels/
├── MSL/
├── PEMS-SF/
├── PSM/
├── SMAP/
├── SMD/
├── SWaT/
├── SelfRegulationSCP1/
├── SelfRegulationSCP2/
├── SpokenArabicDigits/
├── UWaveGestureLibrary/
├── electricity/
├── exchange_rate/
├── illness/
├── m4/
├── traffic/
├── weather/
└── .gitattributes

Usage

You can load the dataset directly using the datasets library:

from datasets import load_dataset
dataset = load_dataset("lalababa/Time-Series-Library", "ETT-small")

License

This dataset is released under the CC BY 4.0 License.

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},
}

@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},
}