Automatic Change-Point Detection in Time Series via Deep Learning

TL;DR

This paper introduces a neural network-based method for automatic change-point detection in time series data, capable of adapting to various change types and outperforming traditional methods in complex noise conditions.

Contribution

It proposes a novel neural network approach for offline change-point detection, with theoretical error bounds and empirical validation demonstrating competitive and superior performance.

Findings

Performs well with limited training data

Outperforms CUSUM in auto-correlated and heavy-tailed noise

Effective in activity change detection with accelerometer data

Abstract

Detecting change-points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated by many existing tests for the presence of a change-point being representable by a simple neural network, and thus a neural network trained with sufficient data should have performance at least as good as these methods. We present theory that quantifies the error rate for such an approach, and how it depends on the amount of training data. Empirical results show that, even with limited training data, its performance is competitive with the standard CUSUM-based classifier for detecting a change in mean when the noise is independent and Gaussian, and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. Our method also shows strong results in detecting and localising changes in activity based on accelerometer data.