Bayesian Approaches to Segmenting a Simple Time Series

Jonathan J. Oliver and Catherine S. Forbes

No 267936, Department of 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. We conclude that the MML criterion is the preferred criterion. We then apply the segmentation method to financial time series data.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 22
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Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267936

DOI: 10.22004/ag.econ.267936

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