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Thoughts and Theory
MiniRocket: Fast(er) and Accurate Time Series Classification
The FASTEST state-of-the-art algorithm for series classification with Python
Most state-of-the-art (SOTA) time series classification methods are limited by high computational complexity. This makes them slow to train on smaller datasets and effectively unusable on large datasets.
Recently, ROCKET (RandOM Convolutional KErnel Transform) has achieved SOTA of accuracy in just a fraction of the time as other SOTA time series classifiers. ROCKET transforms time series into features using random convolutional kernels and passes the features to a linear classifier.
MiniRocket is even faster!
MiniRocket (MINImally RandOm Convolutional KErnel Transform) is a (nearly) deterministic reformulation of Rocket that is 75 times faster on larger datasets and boasts roughly equivalent accuracy.
MiniRocket is the new default variant of Rocket
On the 108 datasets in the UCR archive, Rocket ran in roughly 2 hours on a single CPU core. MINIROCKET took only 8 minutes to run! For comparison, the next fastest SOTA algorithm (cBOSS) took approximately 14 hours.
