Off-line detection of multiple change points with the Filtered Derivative with p-Value method

TL;DR

This paper introduces an off-line change point detection method combining Filtered Derivative with p-value testing, offering efficient and accurate segmentation for various time series applications.

Contribution

It enhances the Filtered Derivative method by incorporating p-value testing to reduce false alarms in change point detection.

Findings

Effective detection of change points in simulated data

Successful application to heartbeat and financial time series

Comparable or improved performance over existing methods

Abstract

This paper deals with off-line detection of change points for time series of independent observations, when the number of change points is unknown. We propose a sequential analysis like method with linear time and memory complexity. Our method is based at first step, on Filtered Derivative method which detects the right change points but also false ones. We improve Filtered Derivative method by adding a second step in which we compute the p-values associated to each potential change points. Then we eliminate as false alarms the points which have p-value smaller than a given critical level. Next, our method is compared with the Penalized Least Square Criterion procedure on simulated data sets. Eventually, we apply Filtered Derivative with p-Value method to segmentation of heartbeat time series, and detection of change points in the average daily volume of financial time series.