  Until recently, the most accurate methods for time series classification were
limited by high computational complexity. ROCKET achieves state-of-the-art
accuracy with a fraction of the computational expense of most existing methods
by transforming input time series using random convolutional kernels, and using
the transformed features to train a linear classifier. We reformulate ROCKET
into a new method, MINIROCKET, making it up to 75 times faster on larger
datasets, and making it almost deterministic (and optionally, with additional
computational expense, fully deterministic), while maintaining essentially the
same accuracy. Using this method, it is possible to train and test a classifier
on all of 109 datasets from the UCR archive to state-of-the-art accuracy in
less than 10 minutes. MINIROCKET is significantly faster than any other method
of comparable accuracy (including ROCKET), and significantly more accurate than
any other method of even roughly-similar computational expense. As such, we
suggest that MINIROCKET should now be considered and used as the default
variant of ROCKET.
