@maorgranot1 for tracking

See
#2351

The problem comes when the shortest series in transform is shorter than the shortest seem in fit. In this case the convolution overruns the the transform data causing and out of bounds exception. so if you remember we had a long discussion about this and did not agree on how best to do it. This issue effects RDST and to a lesser extent the shapelet transform too.

Solutions considered and rejected in discussion were

1. throw an exception: this would happen approximately 50% of the time, so is not really exceptional
2. Refit kernels in transform: this breaks the basic "transform does not change estimator model" and is confusing
3. Shorten over long dilations/kernels in transform. Also considered unacceptable as basically unexpected behaviour

basically its an algorithmic component on how to do this and the there is no published way of doing this. The legacy multivariate/minirocket approach involved concatenation over channels and zero padding, was more arbitrary than the above solutions.

discussed this with @angus924 today, we agreed the best way to handle it was to pad any series shorter than the shortest seem in fit. Any thoughts @baraline @MatthewMiddlehurst?

Seems like a resonable strategy, the other alternative are not very satisfying anyway. Throwing a warning when this happens to notify the user of it happening in the background and this will do it IMO

@TonyBagnall just to confirm, we create kernels based on the shortest series in fit then if we pad to shortests during transform?