Functional Change Point Detection via Adjacent Deviation Subspace

TL;DR

This paper introduces the Adjacent Deviation Subspace (ADS), a new framework for functional change point detection that reduces data dimensionality while maintaining critical information, improving accuracy and interpretability.

Contribution

The paper proposes the ADS framework and an efficient dimension reduction operator that enhances change point detection in functional data, overcoming limitations of traditional FPCA.

Findings

ADS effectively preserves change point information.

The method outperforms existing approaches in simulations.

Provides intuitive visualization of change points.

Abstract

This paper develops the concept of the Adjacent Deviation Subspace (ADS), a novel framework for reducing infinite-dimensional functional data into finite-dimensional vector or scalar representations while preserving critical information of functional change points. To identify this functional subspace, we propose an efficient dimension reduction operator that overcomes the critical limitation of information loss inherent in traditional functional principal component analysis (FPCA). Building upon this foundation, we first construct a test statistic based on the dimension-reducing target operator to test the existence of change points. Second, we present the MPULSE criterion to estimate change point locations in lower-dimensional representations. This approach not only reduces computational complexity and mitigates false positives but also provides intuitive graphical visualization of change point locations. Lastly, we establish a unified analytical framework that seamlessly integrates dimension reduction with precise change point detection. Extensive simulation studies and real-world data applications validate the robustness and efficacy of our method, consistently demonstrating superior performance over existing approaches.