Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package

TL;DR

The paper introduces the R package VARDetect, which implements efficient algorithms for detecting multiple change points in structured VAR models, aiding analysis of multivariate time series with structural shifts.

Contribution

The paper presents novel algorithms within the VARDetect package for fast change point detection in structured VAR models, accommodating sparse and low-rank plus sparse transition matrices.

Findings

Algorithms with sublinear and linear complexity for change point detection.

Support for structured sparse and low-rank plus sparse VAR models.

Tools for data simulation and visualization of change points.

Abstract

Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics of multivariate time series data. They have been widely used in macroeconomics, financial econometrics, neuroscience and functional genomics. In many applications, the data exhibit structural changes in their autoregressive dynamics, which correspond to changes in the transition matrices of the VAR model that specify such dynamics. We present the R package VARDetect that implements two classes of algorithms to detect multiple change points in piecewise stationary VAR models. The first exhibits sublinear computational complexity in the number of time points and is best suited for structured sparse models, while the second exhibits linear time complexity and is designed for models whose transition matrices are assumed to have a low rank plus sparse decomposition. The package also has functions to generate data from the various variants of VAR models discussed, which is useful in simulation studies, as well as to visualize the results through network layouts.