Package: tsdb
Welcome to TSDBa Python toolbox to ease loading public time-series datasets📣 TSDB now supports a total of 1️⃣6️⃣9️⃣ time-series datasets ‼️TSDB is a part ofPyPOTS(a Python toolbox for data mining on Partially-Observed Time Series), and was separated from PyPOTS for decoupling datasets from learning algorithms.TSDB is created to help researchers and engineers get rid of data collecting and downloading, and focus back on data processing details. TSDB provides all-in-one-stop convenience for downloading and loading open-source time-series datasets (available datasets listedbelow).❗️Please note that due to people have very different requirements for data processing, data-loading functions in TSDB only contain the most general steps (e.g. removing invalid samples) and won't process the data (not even normalize it). So, no worries, TSDB won't affect your data preprocessing. If you only want the raw datasets, TSDB can help you download and save raw datasets as well (take a look atUsage Examplesbelow).🤝 If you need TSDB to integrate an open-source dataset or want to add it into TSDB yourself, please feel free to request for it by creating an issue or make a PR to merge your code.🤗Pleasestar this repo to help others notice TSDB if you think it is a useful toolkit.Pleaseproperlycite TSDB and PyPOTSin your publications
if it helps with your research. This really means a lot to our open-source research. Thank you!❖ Usage ExamplesTSDB now is available on❗️Install it withconda install tsdb, you may need to specify the channel with option-c conda-forgeor install from PyPI:pip install tsdbor install from source code:pip installhttps://github.com/WenjieDu/TSDB/archive/main.zipimporttsdb# list all available datasets in TSDBtsdb.list_available_datasets()# select the dataset you need and load it, TSDB will download, extract, and process it automaticallydata=tsdb.load('physionet_2012')# if you need the raw data, use download_and_extract()tsdb.download_and_extract('physionet_2012','./save_it_here')# datasets you once loaded are cached, and you can check them with list_cached_data()tsdb.list_cache()# you can delete only one specific dataset and preserve otherstsdb.delete_cache(dataset_name='physionet_2012')# or you can delete all cache with delete_cached_data() to free disk spacetsdb.delete_cache()# to avoid taking up too much space if downloading many datasets,# TSDB cache directory can be migrated to an external disktsdb.migrate_cache("/mnt/external_disk/TSDB_cache")That's all. Simple and efficient. Enjoy it! 😃❖ List of Available DatasetsNameMain TasksPhysioNet Challenge 2012Forecasting, Imputation, ClassificationPhysioNet Challenge 2019Forecasting, Imputation, ClassificationBeijing Multi-Site Air-QualityForecasting, ImputationElectricity Load DiagramsForecasting, ImputationElectricity Transformer Temperature (ETT)Forecasting, ImputationVessel AISForecasting, Imputation, ClassificationUCR & UEA Datasets(all 163 datasets)Classification❖ Citing TSDB/PyPOTSThe paper introducing PyPOTS project is available on arXiv atthis URL,
and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track forMachine Learning Open Source Software). If you use TSDB in your work,
please cite PyPOTS project as below and 🌟star this repository to make others notice this library. 🤗 Thank you!@article{du2023PyPOTS,title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},author={Wenjie Du},year={2023},eprint={2305.18811},archivePrefix={arXiv},primaryClass={cs.LG},url={https://arxiv.org/abs/2305.18811},doi={10.48550/arXiv.2305.18811},}Wenjie Du. (2023).
PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
arXiv, abs/2305.18811.https://arxiv.org/abs/2305.18811or@inproceedings{du2023PyPOTS,title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},booktitle={9th SIGKDD workshop on Mining and Learning from Time Series (MiLeTS'23)},author={Wenjie Du},year={2023},url={https://arxiv.org/abs/2305.18811},}Wenjie Du. (2023).
PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
In9th SIGKDD workshop on Mining and Learning from Time Series (MiLeTS'23).https://arxiv.org/abs/2305.18811🏠 Visits