Segmentation of multiple series using a Lasso strategy

TL;DR

This paper introduces a semi-parametric method combining Dynamic Programming and Lasso estimators for joint segmentation of multiple series with functional parts, improving change detection and periodic variation estimation in geoscience data.

Contribution

It presents a novel iterative approach that integrates Lasso-based functional estimation with segmentation, offering greater flexibility and accuracy over previous methods.

Findings

Effective in detecting abrupt changes in GPS series

Accurately estimates periodic variations in geodesy data

Demonstrated on real GPS station data from Australia

Abstract

We propose a new semi-parametric approach to the joint segmentation of multiple series corrupted by a functional part. This problem appears in particular in geodesy where GPS permanent station coordinate series are affected by undocumented artificial abrupt changes and additionally show prominent periodic variations. Detecting and estimating them are crucial, since those series are used to determine averaged reference coordinates in geosciences and to infer small tectonic motions induced by climate change. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the bias part and improvements in the segmentation. The performance of our method is assessed using simulated and real data. In particular, we apply our method to data from four GPS stations in Yarragadee, Australia. Our estimation procedure results to be a reliable tool to assess series in terms of change detection and periodic variations estimation giving an interpretable estimation of the functional part of the model in terms of known functions.