  Rocket and MiniRocket, while two of the fastest methods for time series
classification, are both somewhat less accurate than the current most accurate
methods (namely, HIVE-COTE and its variants). We show that it is possible to
significantly improve the accuracy of MiniRocket (and Rocket), with some
additional computational expense, by expanding the set of features produced by
the transform, making MultiRocket (for MiniRocket with Multiple Features)
overall the single most accurate method on the datasets in the UCR archive,
while still being orders of magnitude faster than any algorithm of comparable
accuracy other than its precursors
