Bake off redux: a review and experimental evaluation of recent time series classification algorithms

In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017 ) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a ‘bake off’, identified that onl...

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Vydané v:Data mining and knowledge discovery Ročník 38; číslo 4; s. 1958 - 2031
Hlavní autori: Middlehurst, Matthew, Schäfer, Patrick, Bagnall, Anthony
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.07.2024
Springer Nature B.V
Predmet:
Algorithms
Archives & records
Artificial Intelligence
Chemistry and Earth Sciences
Classification
Computer Science
Data mining
Data Mining and Knowledge Discovery
Datasets
Information Storage and Retrieval
Machine learning
Performance evaluation
Physics
Statistics for Engineering
Taxonomy
Time series
Bake off
ROCKET
UCR Archive
Time series classification
HIVE-COTE
ISSN:1384-5810, 1573-756X
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Shrnutí:In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017 ) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a ‘bake off’, identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of feature they extract from time series data, forming a taxonomy of five main algorithm types. This categorisation of algorithms alongside the provision of code and accessible results for reproducibility has helped fuel an increase in popularity of the TSC field. Over six years have passed since this bake off, the UCR archive has expanded to 112 datasets and there have been a large number of new algorithms proposed. We revisit the bake off, seeing how each of the proposed categories have advanced since the original publication, and evaluate the performance of newer algorithms against the previous best-of-category using an expanded UCR archive. We extend the taxonomy to include three new categories to reflect recent developments. Alongside the originally proposed distance, interval, shapelet, dictionary and hybrid based algorithms, we compare newer convolution and feature based algorithms as well as deep learning approaches. We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category. Overall, we find that two recently proposed algorithms, MultiROCKET+Hydra (Dempster et al. 2022 ) and HIVE-COTEv2 (Middlehurst et al. Mach Learn 110:3211-3243. 2021 ), perform significantly better than other approaches on both the current and new TSC problems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-024-01022-1