Learning Conditional Independence Differential Graphs From Time-Dependent Data

TL;DR

This paper introduces a novel method for estimating differences in conditional independence graphs of two time series Gaussian graphical models by accounting for temporal dependencies, using penalized spectral domain techniques and ADMM optimization.

Contribution

It proposes a frequency domain penalized approach with convex and non-convex penalties for differential graph estimation in time-dependent data, with theoretical consistency guarantees.

Findings

Log-sum penalty outperforms lasso in synthetic data

Proposed method outperforms i.i.d. approaches in accuracy

Method validated on synthetic and real datasets

Abstract

Estimation of differences in conditional independence graphs (CIGs) of two time series Gaussian graphical models (TSGGMs) is investigated where the two TSGGMs are known to have similar structure. The TSGGM structure is encoded in the inverse power spectral density (IPSD) of the time series. In several existing works, one is interested in estimating the difference in two precision matrices to characterize underlying changes in conditional dependencies of two sets of data consisting of independent and identically distributed (i.i.d.) observations. In this paper we consider estimation of the difference in two IPSDs to characterize the underlying changes in conditional dependencies of two sets of time-dependent data. Our approach accounts for data time dependencies unlike past work. We analyze a penalized D-trace loss function approach in the frequency domain for differential graph learning, using Wirtinger calculus. We consider both convex (group lasso) and non-convex (log-sum and SCAD group penalties) penalty/regularization functions. An alternating direction method of multipliers (ADMM) algorithm is presented to optimize the objective function. We establish sufficient conditions in a high-dimensional setting for consistency (convergence of the inverse power spectral density to true value in the Frobenius norm) and graph recovery. Both synthetic and real data examples are presented in support of the proposed approaches. In synthetic data examples, our log-sum-penalized differential time-series graph estimator significantly outperformed our lasso based differential time-series graph estimator which, in turn, significantly outperformed an existing lasso-penalized i.i.d. modeling approach, with $F_1$ score as the performance metric.