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Keywords:

  • change point;
  • Bayesian analysis;
  • time series;
  • dynamic programming

Abstract

Given distinct climatic periods in the various facets of the Earth's climate system, many attempts have been made to determine the exact timing of ‘change points’ or regime boundaries. However, identification of change points is not always a simple task. A time series containing N data points has approximately Nk distinct placements of k change points, rendering brute force enumeration futile as the length of the time series increases. Moreover, how certain are we that any one placement of change points is superior to the rest? This paper introduces a Bayesian Change Point algorithm which provides uncertainty estimates both in the number and location of change points through an efficient probabilistic solution to the multiple change point problem. To illustrate its versatility, the Bayesian Change Point algorithm is used to analyse both the NOAA/NCDC annual global surface temperature anomalies time series and the much longer δ18O record of the Plio-Pleistocene. Copyright © 2012 Royal Meteorological Society