tsdb 0.2.1

TSDB (Time Series Data Beans): a Python toolbox helping load open-source time-series datasets

Welcome to TSDB

a Python toolbox to ease loading public time-series datasets

📣 TSDB now supports a total of 1️⃣6️⃣8️⃣ time-series datasets ‼️

TSDB is a part of PyPOTS project (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 listed below).

❗️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 at Usage Examples below).

🤝 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.

🤗 Please star this repo to help others notice TSDB if you think it is a useful toolkit. Please properly cite TSDB and PyPOTS in your publications if it helps with your research. This really means a lot to our open-source research. Thank you!

❖ Usage Examples

TSDB now is available on ❗️

Install it with conda install tsdb , you may need to specify the channel with option -c conda-forge

or install from PyPI:

pip install tsdb

or install from source code:

pip install https://github.com/WenjieDu/TSDB/archive/main.zip

import tsdb

tsdb.list_available_datasets()  # list all available datasets in TSDB
data = tsdb.load(
    'physionet_2012')  # select the dataset you need and load it, TSDB will download, extract, and process it automatically
tsdb.download_and_extract('physionet_2012', './save_it_here')  # if you need the raw data, use download_and_extract()
tsdb.list_cache()  # datasets you once loaded are cached, and you can check them with list_cached_data()
tsdb.delete_cache(dataset_name='physionet_2012')  # you can delete only one specific dataset and preserve others
tsdb.delete_cache()  # or you can delete all cache with delete_cached_data() to free disk space

That's all. Simple and efficient. Enjoy it! 😃

❖ List of Available Datasets

Name	Main Tasks
PhysioNet Challenge 2012	Classification, Forecasting, Imputation
PhysioNet Challenge 2019	Classification, Imputation
Beijing Multi-Site Air-Quality	Forecasting, Imputation
Electricity Load Diagrams	Forecasting, Imputation
UCR & UEA Datasets (all 163 datasets)	Classification
Vessel AIS	Classification, Forecasting, Imputation

❖ Citing TSDB/PyPOTS

The paper introducing PyPOTS project is available on arXiv at this URL, and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for Machine 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/2305.18811

or

@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. In 9th SIGKDD workshop on Mining and Learning from Time Series (MiLeTS'23). https://arxiv.org/abs/2305.18811

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