| --- |
| tags: |
| - time-series |
| - forecasting |
| - anomaly-detection |
| - classification |
| - TSLib |
| license: cc-by-4.0 |
| task_categories: |
| - time-series-forecasting |
| pretty_name: Time-Series-Library (TSLib) |
| language: |
| - en |
| configs: |
| - config_name: ETTh1 |
| description: ETT long-term forecasting subset ETTh1 (hourly). |
| data_files: |
| - ETT-small/ETTh1.csv |
| - config_name: ETTh2 |
| description: ETT long-term forecasting subset ETTh2 (hourly). |
| data_files: |
| - ETT-small/ETTh2.csv |
| - config_name: ETTm1 |
| description: ETT long-term forecasting subset ETTm1 (15-min). |
| data_files: |
| - ETT-small/ETTm1.csv |
| - config_name: ETTm2 |
| description: ETT long-term forecasting subset ETTm2 (15-min). |
| data_files: |
| - ETT-small/ETTm2.csv |
| - config_name: electricity |
| description: Electricity load forecasting (UCI Electricity). |
| data_files: |
| - electricity/electricity.csv |
| - config_name: traffic |
| description: Traffic volume forecasting. |
| data_files: |
| - traffic/traffic.csv |
| - config_name: weather |
| description: Weather time-series forecasting. |
| data_files: |
| - weather/weather.csv |
| - config_name: exchange_rate |
| description: Exchange rate forecasting. |
| data_files: |
| - exchange_rate/exchange_rate.csv |
| - config_name: national_illness |
| description: Influenza-like illness (ILI) forecasting. |
| data_files: |
| - illness/national_illness.csv |
| - config_name: m4-yearly |
| description: M4 Yearly forecasting subset. |
| data_files: |
| - split: train |
| path: m4/Yearly-train.csv |
| - split: test |
| path: m4/Yearly-test.csv |
| - config_name: m4-quarterly |
| description: M4 Quarterly forecasting subset. |
| data_files: |
| - split: train |
| path: m4/Quarterly-train.csv |
| - split: test |
| path: m4/Quarterly-test.csv |
| - config_name: m4-monthly |
| description: M4 Monthly forecasting subset. |
| data_files: |
| - split: train |
| path: m4/Monthly-train.csv |
| - split: test |
| path: m4/Monthly-test.csv |
| - config_name: m4-weekly |
| description: M4 Weekly forecasting subset. |
| data_files: |
| - split: train |
| path: m4/Weekly-train.csv |
| - split: test |
| path: m4/Weekly-test.csv |
| - config_name: m4-daily |
| description: M4 Daily forecasting subset. |
| data_files: |
| - split: train |
| path: m4/Daily-train.csv |
| - split: test |
| path: m4/Daily-test.csv |
| - config_name: m4-hourly |
| description: M4 Hourly forecasting subset. |
| data_files: |
| - split: train |
| path: m4/Hourly-train.csv |
| - split: test |
| path: m4/Hourly-test.csv |
| - config_name: EthanolConcentration |
| description: 'UEA multivariate classification: EthanolConcentration.' |
| data_files: |
| - split: train |
| path: EthanolConcentration/EthanolConcentration_TRAIN.ts |
| - split: test |
| path: EthanolConcentration/EthanolConcentration_TEST.ts |
| - config_name: FaceDetection |
| description: 'UEA multivariate classification: FaceDetection.' |
| data_files: |
| - split: train |
| path: FaceDetection/FaceDetection_TRAIN.ts |
| - split: test |
| path: FaceDetection/FaceDetection_TEST.ts |
| - config_name: Handwriting |
| description: 'UEA multivariate classification: Handwriting.' |
| data_files: |
| - split: train |
| path: Handwriting/Handwriting_TRAIN.ts |
| - split: test |
| path: Handwriting/Handwriting_TEST.ts |
| - config_name: Heartbeat |
| description: 'UEA multivariate classification: Heartbeat.' |
| data_files: |
| - split: train |
| path: Heartbeat/Heartbeat_TRAIN.ts |
| - split: test |
| path: Heartbeat/Heartbeat_TEST.ts |
| - config_name: JapaneseVowels |
| description: 'UEA multivariate classification: JapaneseVowels.' |
| data_files: |
| - split: train |
| path: JapaneseVowels/JapaneseVowels_TRAIN.ts |
| - split: test |
| path: JapaneseVowels/JapaneseVowels_TEST.ts |
| - config_name: PEMS-SF |
| description: 'UEA multivariate classification: PEMS-SF.' |
| data_files: |
| - split: train |
| path: PEMS-SF/PEMS-SF_TRAIN.ts |
| - split: test |
| path: PEMS-SF/PEMS-SF_TEST.ts |
| - config_name: SelfRegulationSCP1 |
| description: 'UEA multivariate classification: SelfRegulationSCP1.' |
| data_files: |
| - split: train |
| path: SelfRegulationSCP1/SelfRegulationSCP1_TRAIN.ts |
| - split: test |
| path: SelfRegulationSCP1/SelfRegulationSCP1_TEST.ts |
| - config_name: SelfRegulationSCP2 |
| description: 'UEA multivariate classification: SelfRegulationSCP2.' |
| data_files: |
| - split: train |
| path: SelfRegulationSCP2/SelfRegulationSCP2_TRAIN.ts |
| - split: test |
| path: SelfRegulationSCP2/SelfRegulationSCP2_TEST.ts |
| - config_name: SpokenArabicDigits |
| description: 'UEA multivariate classification: SpokenArabicDigits.' |
| data_files: |
| - split: train |
| path: SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts |
| - split: test |
| path: SpokenArabicDigits/SpokenArabicDigits_TEST.ts |
| - config_name: UWaveGestureLibrary |
| description: 'UEA multivariate classification: UWaveGestureLibrary.' |
| data_files: |
| - split: train |
| path: UWaveGestureLibrary/UWaveGestureLibrary_TRAIN.ts |
| - split: test |
| path: UWaveGestureLibrary/UWaveGestureLibrary_TEST.ts |
| - config_name: SMD-data |
| description: Server Machine Dataset (SMD) for anomaly detection — train & test data. |
| data_files: |
| - split: train |
| path: SMD/SMD_train.npy |
| - split: test |
| path: SMD/SMD_test.npy |
| - config_name: SMD-label |
| description: Server Machine Dataset (SMD) — test anomaly labels. |
| data_files: |
| - split: test_label |
| path: SMD/SMD_test_label.npy |
| - config_name: MSL-data |
| description: NASA Mars Science Laboratory (MSL) anomaly detection — train/test arrays. |
| data_files: |
| - split: train |
| path: MSL/MSL_train.npy |
| - split: test |
| path: MSL/MSL_test.npy |
| - config_name: MSL-label |
| description: MSL anomaly detection — test labels. |
| data_files: |
| - split: test_label |
| path: MSL/MSL_test_label.npy |
| - config_name: SMAP-data |
| description: >- |
| NASA Soil Moisture Active Passive (SMAP) anomaly detection — train/test |
| arrays. |
| data_files: |
| - split: train |
| path: SMAP/SMAP_train.npy |
| - split: test |
| path: SMAP/SMAP_test.npy |
| - config_name: SMAP-label |
| description: SMAP anomaly detection — test labels. |
| data_files: |
| - split: test_label |
| path: SMAP/SMAP_test_label.npy |
| - config_name: PSM-data |
| description: KPI-based Process/System Monitoring data (train/test). |
| data_files: |
| - split: train |
| path: PSM/train.csv |
| - split: test |
| path: PSM/test.csv |
| - config_name: PSM-label |
| description: KPI-based Process/System Monitoring labels (test_label). |
| data_files: |
| - split: test_label |
| path: PSM/test_label.csv |
| - config_name: SWaT |
| description: Secure Water Treatment (SWaT) anomaly detection, processed data. |
| data_files: |
| - split: train |
| path: SWaT/swat_train2.csv |
| - split: test |
| path: SWaT/swat2.csv |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
|
|
| # 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](https://arxiv.org/abs/2407.13278)**. |
|
|
| To get started with the codebase and contribute, please visit the **[GitHub repository](https://github.com/thuml/Time-Series-Library)**. |
|
|
| ## 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}, |
| } |
| ``` |
