Change Point Detection for Functional Autoregressive Processes on the Sphere
Methodology
2025-12-04 v1
Abstract
We introduce a novel framework for change point detection in spherical functional autoregressive (SPHAR) processes, enabling the identification of structural breaks in spatio-temporal random fields on the sphere. Our LASSO-regularized estimator, based on penalized dynamic programming in the harmonic domain, operates without knowledge of the number or locations of change points and offers non-asymptotic theoretical guarantees. This approach provides a new tool for analyzing nonstationary phenomena on the sphere, relevant to climate science, cosmology, and beyond.
Cite
@article{arxiv.2512.03255,
title = {Change Point Detection for Functional Autoregressive Processes on the Sphere},
author = {Federica Spoto and Alessia Caponera and Pierpaolo Brutti},
journal= {arXiv preprint arXiv:2512.03255},
year = {2025}
}