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The all-relevant problem of feature selection is the identification of allstrongly and weakly relevant attributes. This problem is especially hard tosolve for time series classification and regression in industrial applicationssuch as predictive maintenance or production line optimization, for which eachlabel or regression target is associated with several time series andmeta-information simultaneously. Here, we are proposing an efficient, scalablefeature extraction algorithm for time series, which filters the availablefeatures in an early stage of the machine learning pipeline with respect totheir significance for the classification or regression task, while controllingthe expected percentage of selected but irrelevant features. The proposedalgorithm combines established feature extraction methods with a featureimportance filter. It has a low computational complexity, allows to start on aproblem with only limited domain knowledge available, can be triviallyparallelized, is highly scalable and based on well studied non-parametrichypothesis tests. We benchmark our proposed algorithm on all binaryclassification problems of the UCR time series classification archive as wellas time series from a production line optimization project and simulatedstochastic processes with underlying qualitative change of dynamics.

Distributed and parallel time series feature extraction for industrial big data applications