ROCKET: a new SOTA in Time Series Classification, now with multivariate and GPU support
Last week, there was a major milestone in the area of Time Series Classification.
A new method, called ROCKET (RandOm Convolutional KErnel Transform) developed by Dempster et al. was released (ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels, paper) together with the code they used.
This new method not only beat the previous recognized state of the art (HIVE-COTE) on a TSC benchmark, but it does it in record time, many orders of magnitude faster than any other method.
I’ve been using it for a couple of days and the results are IMPRESSIVE!!
The release code however has 2 limitations:
- it can only handle univariate time series
- it doesn’t support GPU
I have developed ROCKET in Pytorch and you can now use it with univariate of multivariate time series, and with a GPU. I have shared a notebook that explains how you can use this new method.
If you are interested in this, you can find a more information here, in the time series/ sequential data study group thread.