Filtered derivative with p-value method for multiple change-points detection

TL;DR

This paper introduces a new off-line change point detection method combining Filtered Derivative with p-value testing, offering efficient and accurate segmentation of time series data, including heartbeat signals.

Contribution

It enhances the Filtered Derivative approach by integrating p-value testing to reduce false positives in change point detection.

Findings

The method has linear time and memory complexity.

It outperforms Penalized Least Square Criterion in simulations.

Successfully applied to heartbeat time series segmentation.

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.