An MDL approach to the climate segmentation problem

TL;DR

This paper introduces an MDL-based method for detecting changepoints in climate time series, accounting for autocorrelation, periodicity, and mean shifts, demonstrated on a century of temperature data.

Contribution

It presents a novel MDL-based framework with a genetic algorithm for changepoint detection in complex climatic data.

Findings

Effective detection of changepoints in temperature series

Method accommodates autocorrelation and periodicity

Applied successfully to a century of temperature data

Abstract

This paper proposes an information theory approach to estimate the number of changepoints and their locations in a climatic time series. A model is introduced that has an unknown number of changepoints and allows for series autocorrelations, periodic dynamics, and a mean shift at each changepoint time. An objective function gauging the number of changepoints and their locations, based on a minimum description length (MDL) information criterion, is derived. A genetic algorithm is then developed to optimize the objective function. The methods are applied in the analysis of a century of monthly temperatures from Tuscaloosa, Alabama.