  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 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,
Hydra+MultiROCKET and HIVE-COTEv2, perform significantly better than other
approaches on both the current and new TSC problems.
