Bayesian Approaches to Segmenting A Simple Time Series

J.J. Oliver and Catherine Forbes

No 14/97, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The segmentation problem arises in many applications in data mining, A.I. and statistics. In this paper, we consider segmenting simple time series. We develop two Bayesian approaches for segmenting a time series, namely the Bayes Factor approach, and the Minimum Message Length (MML) approach. We perform simulations comparing these Bayesian approaches, and then perform a comparison with other classical approaches, namely AIC, MDL and BIC.

Keywords: ECONOMETRICS; TIME SERIES (search for similar items in EconPapers)
JEL-codes: C11 C22 (search for similar items in EconPapers)
Pages: 20 pages
Date: 1997
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Citations: View citations in EconPapers (1)

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