docs: update README for Hugging Face dataset

README.md

@@ -1 +1,97 @@
+---
+tags:
+- time-series
+- forecasting
+- anomaly-detection
+- classification
+- TSLib
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+- anomaly-detection
+- classification
+pretty_name: Time-Series-Library (TSLib)
+language:
+- en
+---
 
+# 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
+```
+
+## 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},
+}
+```