WITHDRAWN Change Points Detection for Spherical Functional Autoregressive Processes

Music City Center 

Every phenomenon can potentially experience transitions in its behavior, making change detection essential for understanding their evolution over time and space. The change point framework is a valuable tool for identifying shifts in dynamic processes and involves estimating the number and location of time points where transitions occur.

A growing area of interest is the study of random fields on the sphere, relevant in astrophysics and climate science, among others. Notably, spherical functional autoregressions (SPHAR(p)) effectively capture random behavior by integrating spatial and temporal dependencies. Detecting structural breaks in spherical random processes is crucial, especially in climate science, where changes in global surface temperature could help describe global warming.

Thanks to the change point framework, we generalize the SPHAR(p) model by relaxing the stationarity assumption. We also introduce a Lasso-based change point detection technique in this setting and assess its effectiveness on both synthetic and real data.

Change-point detection

Spherical random fields

Autoregressive processes

Functional analysis

Lasso 

Section on Statistical Learning and Data Science