Title: Channel masking for multivariate time series shapelets,
Abstract: Time series shapelets are discriminative sub-sequences and their similarity
to time series can be used for time series classification. Initial shapelet
extraction algorithms searched shapelets by complete enumeration of all
possible data sub-sequences. Research on shapelets for univariate time series
proposed a mechanism called shapelet learning which parameterizes the shapelets
and learns them jointly with a prediction model in an optimization procedure.
Trivial extension of this method to multivariate time series does not yield
very good results due to the presence of noisy channels which lead to
overfitting. In this paper we propose a shapelet learning scheme for
multivariate time series in which we introduce channel masks to discount noisy
channels and serve as an implicit regularization.