Graphical estimation of multivariate count time series

TL;DR

This paper introduces a novel graphical modeling approach for multivariate count time series, using a regularized likelihood maximization with an innovative MCEM algorithm to infer dependencies and causality in complex data like disease spread.

Contribution

It proposes a new MCEM algorithm for regularized estimation of partial correlation and causality graphs in multivariate count time series, with proven convergence and practical application to dengue data.

Findings

Identified key wards acting as dengue spread epicenters.

Quantified interdependencies between wards in disease proliferation.

Demonstrated the algorithm's effectiveness on simulated and real data.

Abstract

The problems of selecting partial correlation and causality graphs for count data are considered. A parameter driven generalized linear model is used to describe the observed multivariate time series of counts. Partial correlation and causality graphs corresponding to this model explain the dependencies between each time series of the multivariate count data. In order to estimate these graphs with tunable sparsity, an appropriate likelihood function maximization is regularized with an l1-type constraint. A novel MCEM algorithm is proposed to iteratively solve this regularized MLE. Asymptotic convergence results are proved for the sequence generated by the proposed MCEM algorithm with l1-type regularization. The algorithm is first successfully tested on simulated data. Thereafter, it is applied to observed weekly dengue disease counts from each ward of Greater Mumbai city. The interdependence of various wards in the proliferation of the disease is characterized by the edges of the inferred partial correlation graph. On the other hand, the relative roles of various wards as sources and sinks of dengue spread is quantified by the number and weights of the directed edges originating from and incident upon each ward. From these estimated graphs, it is observed that some special wards act as epicentres of dengue spread even though their disease counts are relatively low.