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In 2017, a research paper 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 onlynine algorithms performed significantly better than the Dynamic Time Warping(DTW) and Rotation Forest benchmarks that were used. The study categorised eachalgorithm by the type of feature they extract from time series data, forming ataxonomy of five main algorithm types. This categorisation of algorithmsalongside the provision of code and accessible results for reproducibility hashelped fuel an increase in popularity of the TSC field. Over six years havepassed since this bake off, the UCR archive has expanded to 112 datasets andthere have been a large number of new algorithms proposed. We revisit the bakeoff, seeing how each of the proposed categories have advanced since theoriginal publication, and evaluate the performance of newer algorithms againstthe previous best-of-category using an expanded UCR archive. We extend thetaxonomy to include three new categories to reflect recent developments.Alongside the originally proposed distance, interval, shapelet, dictionary andhybrid based algorithms, we compare newer convolution and feature basedalgorithms as well as deep learning approaches. We introduce 30 classificationdatasets either recently donated to the archive or reformatted to the TSCformat, and use these to further evaluate the best performing algorithm fromeach category. Overall, we find that two recently proposed algorithms,Hydra+MultiROCKET and HIVE-COTEv2, perform significantly better than otherapproaches on both the current and new TSC problems.

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