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

TL;DR

This paper introduces a scalable, parallel feature extraction algorithm for time series data in industrial applications, improving relevance filtering and computational efficiency for predictive maintenance and production optimization.

Contribution

It presents a novel, scalable, and parallelizable feature extraction method combining established techniques with feature importance filtering, suitable for industrial big data applications.

Findings

Efficient filtering of relevant features in time series data.

High scalability and parallelization capability.

Validated on diverse industrial and benchmark datasets.

Abstract

The all-relevant problem of feature selection is the identification of all strongly and weakly relevant attributes. This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously. Here, we are proposing an efficient, scalable feature extraction algorithm for time series, which filters the available features in an early stage of the machine learning pipeline with respect to their significance for the classification or regression task, while controlling the expected percentage of selected but irrelevant features. The proposed algorithm combines established feature extraction methods with a feature importance filter. It has a low computational complexity, allows to start on a problem with only limited domain knowledge available, can be trivially parallelized, is highly scalable and based on well studied non-parametric hypothesis tests. We benchmark our proposed algorithm on all binary classification problems of the UCR time series classification archive as well as time series from a production line optimization project and simulated stochastic processes with underlying qualitative change of dynamics.