Sequential Detection of a Temporary Change in Multivariate Time Series

TL;DR

This paper introduces TE-CUSUM, a new sequential detection algorithm designed to efficiently identify temporary changes in multivariate time series, especially useful for sensor networks detecting passing events in noisy environments.

Contribution

The paper presents a novel adaptive TE-CUSUM algorithm specifically tailored for detecting temporary changes in multivariate signals, addressing limitations of existing methods.

Findings

TE-CUSUM achieves high detection rates in noisy sensor environments.

The adaptive aggregation improves detection of passing events.

The method is simple and computationally efficient.

Abstract

In this work, we aim to provide a new and efficient recursive detection method for temporarily monitored signals. Motivated by the case of the propagation of an event over a field of sensors, we assumed that the change in the statistical properties in the monitored signals can only be temporary. Unfortunately, to our best knowledge, existing recursive and simple detection techniques such as the ones based on the cumulative sum (CUSUM) do not consider the temporary aspect of the change in a multivariate time series. In this paper, we propose a novel simple and efficient sequential detection algorithm, named Temporary-Event-CUSUM (TE-CUSUM). By combining with a new adaptive way to aggregate local CUSUM variables from each data stream, we empirically show that the TE-CUSUM has a very good detection rate in the case of an event passing through a field of sensors in a very noisy environment.