Simultaneously detecting spatiotemporal changes with penalized Poisson regression models

TL;DR

This paper introduces a novel penalized Poisson regression approach for detecting simultaneous spatiotemporal change points and clusters in large-scale count data, with proven consistency and superior performance.

Contribution

The study develops a new doubly fused penalization method for concurrent change point detection and clustering in spatiotemporal data, along with an efficient estimation algorithm and theoretical validation.

Findings

Method outperforms existing approaches in accuracy and efficiency.

Establishes statistical consistency of the proposed estimator.

Validated through extensive numerical experiments.

Abstract

In the realm of large-scale spatiotemporal data, abrupt changes are commonly occurring across both spatial and temporal domains. This study aims to address the concurrent challenges of detecting change points and identifying spatial clusters within spatiotemporal count data. We introduce an innovative method based on the Poisson regression model, employing doubly fused penalization to unveil the underlying spatiotemporal change patterns. To efficiently estimate the model, we present an iterative shrinkage and threshold based algorithm to minimize the doubly penalized likelihood function. We establish the statistical consistency properties of the proposed estimator, confirming its reliability and accuracy. Furthermore, we conduct extensive numerical experiments to validate our theoretical findings, thereby highlighting the superior performance of our method when compared to existing competitive approaches.