The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914412177129472 |
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| author | Middlehurst, Matthew Rushbrooke, Aiden Ismail-Fawaz, Ali Devanne, Maxime Forestier, Germain Dempster, Angus Webb, Geoffrey I. Holder, Christopher Bagnall, Anthony |
| author_facet | Middlehurst, Matthew Rushbrooke, Aiden Ismail-Fawaz, Ali Devanne, Maxime Forestier, Germain Dempster, Angus Webb, Geoffrey I. Holder, Christopher Bagnall, Anthony |
| contents | Time series machine learning (TSML) is a growing research field that spans a wide range
of tasks. The popularity of established tasks such as classification, clustering, and
extrinsic regression has, in part, been driven by the availability of benchmark datasets.
An archive of 30 multivariate time series classification datasets, introduced in 2018 and
commonly known as the UEA archive, has since become an essential resource cited in hundreds
of publications. We present a substantial expansion of this archive that more than quadruples
its size, from 30 to 133 classification problems. We also release preprocessed versions of
datasets containing missing values or unequal length series, bringing the total number of
datasets to 147. Reflecting the growth of the archive and the broader community, we rebrand
it as the Multiverse archive to capture its diversity of domains. The Multiverse archive
includes datasets from multiple sources, consolidating other collections and standalone
datasets into a single, unified repository. Recognising that running experiments across the
full archive is computationally demanding, we recommend a subset of the full archive called
Multiverse-core (MV-core) for initial exploration. To support researchers in using the new
archive, we provide detailed guidance and a baseline evaluation of established and recent
classification algorithms, establishing performance benchmarks for future research. We have
created a dedicated repository for the Multiverse archive that provides a common aeon
and scikit-learn compatible framework for reproducibility, an extensive record of
published results, and an interactive interface to explore the results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20352 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification Middlehurst, Matthew Rushbrooke, Aiden Ismail-Fawaz, Ali Devanne, Maxime Forestier, Germain Dempster, Angus Webb, Geoffrey I. Holder, Christopher Bagnall, Anthony Machine Learning Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrinsic regression has, in part, been driven by the availability of benchmark datasets. An archive of 30 multivariate time series classification datasets, introduced in 2018 and commonly known as the UEA archive, has since become an essential resource cited in hundreds of publications. We present a substantial expansion of this archive that more than quadruples its size, from 30 to 133 classification problems. We also release preprocessed versions of datasets containing missing values or unequal length series, bringing the total number of datasets to 147. Reflecting the growth of the archive and the broader community, we rebrand it as the Multiverse archive to capture its diversity of domains. The Multiverse archive includes datasets from multiple sources, consolidating other collections and standalone datasets into a single, unified repository. Recognising that running experiments across the full archive is computationally demanding, we recommend a subset of the full archive called Multiverse-core (MV-core) for initial exploration. To support researchers in using the new archive, we provide detailed guidance and a baseline evaluation of established and recent classification algorithms, establishing performance benchmarks for future research. We have created a dedicated repository for the Multiverse archive that provides a common aeon and scikit-learn compatible framework for reproducibility, an extensive record of published results, and an interactive interface to explore the results. |
| title | The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.20352 |