Change-points analysis for generalized integer-valued autoregressive model via minimum description length principle

TL;DR

This paper develops a method for detecting change-points in nonstationary count time series using MCP-GINAR models and MDL principle, employing genetic algorithms with simulated annealing for optimization.

Contribution

It introduces a novel approach combining MCP-GINAR models with MDL for change-point detection, and proposes an improved optimization algorithm using genetic algorithms with simulated annealing.

Findings

The method shows excellent empirical performance in simulations.

It successfully detects change-points in real data examples.

Theoretical consistency of the MDL-based model selection is established.

Abstract

This article considers the problem of modeling a class of nonstationary count time series using multiple change-points generalized integer-valued autoregressive (MCP-GINAR) processes. The minimum description length principle (MDL) is applied to study the statistical inference for the MCP-GINAR model, and the consistency results of the MDL model selection procedure are established respectively under the condition of known and unknown number of change-points. To find the ``best" combination of the number of change-points, the locations of change-points, the order of each segment and its parameters, a genetic algorithm with simulated annealing is implemented to solve this difficult optimization problem. In particular, the simulated annealing process makes up for the precocious problem of the traditional genetic algorithm. Numerical results from simulation experiments and three examples of real data analyses show that the procedure has excellent empirical properties.