Proceedings will be published as Lecture Notes in Computer Science. Previous versions are here .
This website is an ongoing project to develop a comprehensive repository for research into time series classification. If you use the results or code, please cite the paper "Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large and Eamonn Keogh, The Great Time Series Classification Bake Off: a Review and Experimental Evaluation of Recent Algorithmic Advances, Data Mining and Knowledge Discovery, 31(3), 2017". Paper Link, Bibtex Link. We are in the process of updating all the results for the new datasets.
If you want to just reference the website, please do so as: "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com".
If you want to donate data, have any queries or problems with any of the datasets or want to give feedback on the website, please raise an issue on the associated Github repo.
If you want to use PyTorch with the tsc.com data, there is a repo to help with formatting here. It links to a python package that downloads and prepares the TSC data sets as PyTorch tensors.The package is independent of anyone associated with this website, and we have not tested it, but its a great idea so we are happy to share, and will try using it soon.
The univariate TSC archive was relaunched in 2018 with 128 datasets.
Weka formatted ARFF files (and .txt files) (about 500 MB).
sktime formatted ts files (about 250 MB).
Weka formatted ARFF files (and .txt files) (about 2 GB).
sktime formatted ts files (about 1.5 GB).