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Time series shapelets are discriminative sub-sequences and their similarityto time series can be used for time series classification. Initial shapeletextraction algorithms searched shapelets by complete enumeration of allpossible data sub-sequences. Research on shapelets for univariate time seriesproposed a mechanism called shapelet learning which parameterizes the shapeletsand learns them jointly with a prediction model in an optimization procedure.Trivial extension of this method to multivariate time series does not yieldvery good results due to the presence of noisy channels which lead tooverfitting. In this paper we propose a shapelet learning scheme formultivariate time series in which we introduce channel masks to discount noisychannels and serve as an implicit regularization.

Channel masking for multivariate time series shapelets