On change point detection using the fused lasso method

TL;DR

This paper investigates the asymptotic behavior of the fused lasso method for change point detection in non-stationary time series, establishing conditions for its consistency and limitations.

Contribution

It provides a theoretical analysis of the fused lasso's asymptotic properties, including conditions for sparse consistency and convergence rates.

Findings

Fused lasso can reliably detect change points when consecutive changes have different signs.

The method's ability to detect the true sparsity pattern depends on the sign consistency of changes.

Optimality conditions are analyzed using brownian bridge theory.

Abstract

In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances. The focus is on segmentation of a non-stationary time series with respect to changes in these model parameters. This change point detection and estimation problem is also referred to as total variation denoising or l1 -mean filtering and has many important applications in most fields of science and engineering. We establish the (approximate) sparse consistency properties, including rate of convergence, of the so-called fused lasso signal approximator (FLSA). We show that this only holds if the sign of the corresponding consecutive changes are all different, and that this estimator is otherwise incapable of correctly detecting the underlying sparsity pattern. The key idea is to notice that the optimality conditions for this problem can be analyzed using techniques related to brownian bridge theory.