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
└── README.md
Usage
You can load the dataset directly using the datasets library:
from datasets import load_dataset
dataset = load_dataset("thuml/Time-Series-Library", "ETTh1")
Or download specific files with hf_hub_download:
from huggingface_hub import hf_hub_download
hf_hub_download("thuml/Time-Series-Library", "ETT-small/ETTh1.csv", repo_type="dataset")
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},
}