  We propose MultiRocket, a fast time series classification (TSC) algorithm
that achieves state-of-the-art performance with a tiny fraction of the time and
without the complex ensembling structure of many state-of-the-art methods.
MultiRocket improves on MiniRocket, one of the fastest TSC algorithms to date,
by adding multiple pooling operators and transformations to improve the
diversity of the features generated. In addition to processing the raw input
series, MultiRocket also applies first order differences to transform the
original series. Convolutions are applied to both representations, and four
pooling operators are applied to the convolution outputs. When benchmarked
using the University of California Riverside TSC benchmark datasets,
MultiRocket is significantly more accurate than MiniRocket, and competitive
with the best ranked current method in terms of accuracy, HIVE-COTE 2.0, while
being orders of magnitude faster.
