The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification

TL;DR

This paper significantly expands the Multivariate Time Series Classification archive, now called the Multiverse archive, providing a comprehensive, diverse, and accessible resource with benchmarks and tools for the TSML research community.

Contribution

It introduces a large, unified, and curated collection of 147 multivariate time series datasets, including preprocessing and benchmarking tools, to advance research and reproducibility in TSML.

Findings

The archive now contains 147 datasets, more than quadrupling the original size.

A baseline evaluation of classification algorithms establishes performance benchmarks.

The repository supports reproducibility with a common framework and interactive exploration tools.

Abstract

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.