Long signal change-point detection

TL;DR

This paper introduces a new, computationally efficient change-point detection algorithm based on the asymptotic distribution of a recent statistic, suitable for very long signals in various fields.

Contribution

It derives the asymptotic distribution of a change-point detection statistic and develops a new algorithm that is efficient for long signals, validated through simulations and real data.

Findings

The new algorithm accurately detects change-points in long signals.

Simulation results show high efficiency and reliability.

The method outperforms existing techniques on benchmark datasets.

Abstract

The detection of change-points in a spatially or time ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detecting change-points. Simulation of its estimated limit distribution leads to a new and computationally efficient change-point detection algorithm, which can be used on very long signals. We assess the algorithm via simulations and on previously benchmarked real-world data sets.