This site contains data, reference results and links to code for Time Series Classification (TSC), Time Series Clustering (TSCL) and Time Series Extrinsic Regression (TSER)
The classification data form part of the UCR time series archive. Regression data are part of the TSER repository. We thank everyone else involved in donating to and maintaining these archives.
The scikit-learn compatible aeon toolkit contains the state of the art algorithms for time series classification. All of the datasets and results stored here are directly accessible in code using aeon.
The Advanced Analytics and Learning on Temporal Data (AALTD) workshop ran for the eighth time on 18th Sept in Turin. The workshop is part of the ECML/PKDD converence and selected papers will be published in LNCS. See, for example, 2022 and 2021 versions.
The Human Activity Segmentation Challenge was part of ECML/PKDD and the AALTD workshop, more info here
Selected Recent TSC Papers
- 25/05/2023:Bake off redux: a review and experimental evaluation of recent time series classification algorithms arXiv, 2023
- 02/05/2023:Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression arXiv, 2023
- 21/03/2023:Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification Data Min. Know. Disc., 2023
- 06/02/2023:Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey arxiv
- 24/01/2023: Parameterizing the cost function of Dynamic Time Warping with application to time series classification arxiv
- 01/09/2022:MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. Data Min. Know. Disc., 2022
- 01/05/2022 HYDRA: Competing convolutional kernels for fast and accurate time series classification arxiv
- 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.
The domain is owned by Tony Bagnall and maintained by his research group to help promote reproducable research. Unfortunately, we never have enough time to do it justice. If you are interested in sponsoring this website so we can develop it further, please get in touch.
If you use the results or code, please cite the latest bake off paper "Matthew Middlehurst, Patrick Schäfer and Anthony Bagnall, "Bake off redux: a review and experimental evaluation of recent time series classification algorithms"
ArXiv (under review)
There are two code repositories associated with this website. The Java based, weka compatible toolkit tsml and the python based, sklearn compatible aeon.
We are in the process of refreshing and expanding the results sections, more information to follow.
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.
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.