Change Point Detection for Functional Autoregressive Processes on the Sphere
By: Federica Spoto, Alessia Caponera, Pierpaolo Brutti
Potential Business Impact:
Finds changes in weather patterns on Earth.
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.
Similar Papers
Change-Points Detection and Support Recovery for Spatially Indexed Functional Data
Methodology
Finds changes in weather patterns across places.
Adaptive Block-Based Change-Point Detection for Sparse Spatially Clustered Data with Applications in Remote Sensing Imaging
Methodology
Find hidden changes in pictures over time.
Detecting changes in the mean of spatial random fields on a regular grid
Methodology
Finds forest changes in satellite pictures.