Feature Extraction for Change-Point Detection using Stationary Subspace Analysis

TL;DR

This paper introduces a feature extraction method based on Stationary Subspace Analysis to improve change-point detection in high-dimensional time series, demonstrating enhanced accuracy in simulations and industrial fault monitoring.

Contribution

It presents the first feature extraction technique specifically designed for change point detection using an extended Stationary Subspace Analysis approach.

Findings

Significantly improved change point detection accuracy with feature extraction.

Effective dimensionality reduction to non-stationary directions enhances detection.

Validated method on synthetic data and industrial fault monitoring.

Abstract

Detecting changes in high-dimensional time series is difficult because it involves the comparison of probability densities that need to be estimated from finite samples. In this paper, we present the first feature extraction method tailored to change point detection, which is based on an extended version of Stationary Subspace Analysis. We reduce the dimensionality of the data to the most non-stationary directions, which are most informative for detecting state changes in the time series. In extensive simulations on synthetic data we show that the accuracy of three change point detection algorithms is significantly increased by a prior feature extraction step. These findings are confirmed in an application to industrial fault monitoring.