  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.
