Robert Lund

Title: Multiple Changepoint Detection in Climate Time Series (slides)

Abstract: This talk presents methods to estimate the number of changepoint time(s) and their locations in time-ordered data sequences when prior information is known about some of the changepoint times. A Bayesian version of a penalized likelihood objective function is developed from minimum description length (MDL) information theory principles. Optimizing the objective function yields estimates of the changepoint number(s) and location(s). Our MDL penalty depends on where the changepoint(s) lie, but not solely on the total number of changepoints (such as classical AIC and BIC penalties). The techniques allow for autocorrelation in the observations and mean shifts at each changepoint time. This scenario arises in climate time series where a ``metadata" record exists documenting some, but not necessarily all, of station move times and instrumentation changes. Applications to climate time series are presented throughout.