Penalized Subgrouping of Heterogeneous Time Series

TL;DR

This paper extends the multi-VAR framework to identify and estimate subgroup-specific dynamics in heterogeneous time series, improving modeling of complex longitudinal data in social and health sciences.

Contribution

It introduces a novel subgrouping extension to the multi-VAR model, enabling detection of shared dynamic patterns across subsets of individuals.

Findings

The subgrouping extension accurately identifies shared dynamics in simulations.

Empirical application demonstrates improved subgroup detection over existing methods.

Results show enhanced modeling of heterogeneity in multivariate time series.

Abstract

Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in multiple-subject time series through structured penalization. In the original multi-VAR proposal, individual-level transition matrices are decomposed into common and unique dynamics, allowing for generalizable and person-specific features. The current project extends this framework to allow additionally for the identification and penalized estimation of subgroup-specific dynamics; that is, patterns of dynamics that are shared across subsets of individuals. The performance of the proposed subgrouping extension is evaluated in the context of both a simulation study and empirical application, and results are compared to alternative methods for subgrouping multiple-subject, multivariate time series.