An MDL approach to the climate segmentation problem
arXiv:1010.1397 · doi:10.1214/09-AOAS289
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.
Published in at http://dx.doi.org/10.1214/09-AOAS289 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
References in corpus (1)
Cited by in corpus (8)
- Changepoint Detection: An Analysis of the Central England Temperature Series
- A robust approach for estimating change-points in the mean of an AR(1) process
- Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling
- Multiple changepoint detection for periodic autoregressive models with an application to river flow analysis
- Robust multiscale estimation of time-average variance for time series segmentation
- Multiple Changepoint Detection with Partial Information on Changepoint Times
- A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals
- A new segmentation method for the homogenisation of GNSS-derived IWV time-series