Random kernels in multivariate Rocket sktime
Does anyone know for when Rocket is applied in the multivariate setting how random kernels are generated?
Namely is a 1-D kernel randomly generated and applied to a randomly selected feature?
Or is a 2-D kernel generated randomly and this kernel is applied using all features?
Neither the original paper nor the documentation of packages that implement the algorithm seem to mention anything about it.
Topic time-series machine-learning
Category Data Science