Welcome to the UEA & UCR Time Series Classification Repository

The for 7th Workshop on Advanced Analytics and Learning on Temporal Data at ECML-PKDD 2022 will be at ECML/PKDD in Grenoble in September.

Proceedings will be published as Lecture Notes in Computer Science. Previous versions are here .

Selected Recent TSC Papers

• 02/07/2021:MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. Data Min. Know. Disc., 2022
• 15/04/2021:HIVE-COTE 2.0: a new meta ensemble for time series classification. Machine Learning, 2021
• 18/12/2020: The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. OPEN ACCESS. Data Min. Know. Disc., 2020
• 16/12/2020: On the Usage and Performance of The Hierarchical Vote Collective of Transformation-based Ensembles version 1.0 (HIVE-COTE 1.0) in proc. AALTD workshop, 2020
• 10/12/2020: The Canonical Interval Forest (CIF) Classifier for Time Series Classification in proc. in IEEE Int. Conf. on Big Data, 2020
• 17/11/2020: Fast and Accurate Time Series Classification Through Supervised Interval Search in proc. IEEE Int. Conf on Data Mining, 2020
• 14/9/2020: InceptionTime: Finding AlexNet for Time Series Classification Data Min. Know. Disc. 2020
• 7/9/2020: The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification in proc. ECML-PKDD, 2020
• 13/6/2020: ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels Data Min. Know. Disc. 34, 1454–1495, 2020
• 5/3/2020 TS-CHIEF: a scalable and accurate forest algorithm for time series classification Data Min. Know. Disc. 34, 2020

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".

TSC Code

There are two code repositories associated with this website. The Java based, weka compatible toolkit tsml and the python based, sklearn compatible sktime.

TSC/TSCL Results

We are in the process of refreshing and expanding the results sections, more information to follow.

Download the tsml classification accuracy results for the 112 UCR univariate TSC problems presented in the univariate bake off and the HC2 paper.

Download the tsml classification accuracy results for the 26 UEA multivariate TSC problems presented in the multivariate bake off and the HC2 paper.

Alternatively you can just download the results directly from the website in code. Tools for downloading, presenting and reproducing these results are in the following GitHub repo. This is a work in progress, we would welcome contributions.

TSC/TSCL Data

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.

The univariate TSC archive was relaunched in 2018 with 128 datasets.

Download all of the new 128 UCR Time Series Classification datasets

Weka formatted ARFF files (and .txt files) (about 500 MB).

sktime formatted ts files (about 250 MB).

more info The univariate TSC archive can be referenced with this paper.

The multivariate TSC archive was launched with 30 datasets in 2018.

Download all of the new 30 multivariate UEA Time Series Classification datasets

Weka formatted ARFF files (and .txt files) (about 2 GB).

sktime formatted ts files (about 1.5 GB).

more info. The archive can be referenced with this paper.

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.