A latent space model for multivariate count data time series analysis

TL;DR

This paper introduces a new latent space model combining multivariate count time series analysis with network-based latent variables, enabling better understanding and prediction of complex temporal count data.

Contribution

The novel framework integrates vector autoregressive models with latent network projections, providing a new perspective and improved interpretability for multivariate count time series.

Findings

Effective in recovering underlying network structures

Accurate future predictions demonstrated in simulations

Provides meaningful interpretations of complex data patterns

Abstract

Motivated by a dataset of burglaries in Chicago, USA, we introduce a novel framework to analyze time series of count data combining common multivariate time series models with latent position network models. This novel methodology allows us to gain a new latent variable perspective on the crime dataset that we consider, allowing us to disentangle and explain the complex patterns exhibited by the data, while providing a natural time series framework that can be used to make future predictions. Our model is underpinned by two well known statistical approaches: a log-linear vector autoregressive model, which is prominent in the literature on multivariate count time series, and a latent projection model, which is a popular latent variable model for networks. The role of the projection model is to characterize the interaction parameters of the vector autoregressive model, thus uncovering the underlying network that is associated with the pairwise relationships between the time series. Estimation and inferential procedures are performed using an optimization algorithm and a Hamiltonian Monte Carlo procedure for efficient Bayesian inference. We also include a simulation study to illustrate the merits of our methodology in recovering consistent parameter estimates, and in making accurate future predictions for the time series. As we demonstrate in our application to the crime dataset, this new methodology can provide very meaningful model-based interpretations of the data, and it can be generalized to other time series contexts and applications.