Bayesian Approaches to Segmenting a Simple Time Series (1997)
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BibTeX
@MISC{Oliver97bayesianapproaches,
author = {Jonathan J. Oliver and Catherine S. Forbes},
title = {Bayesian Approaches to Segmenting a Simple Time Series},
year = {1997}
}
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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. 1 Introduction In this paper, we consider the problem of segmenting simple time series. We consider time series of the form: y t+1 = y t + ¯ j + ffl t where we are given N data points (y 1 : : : ; yN ) and we assume there are C + 1 segments (j 2 f0; : : : Cg), and that each ffl t is Gaussian with mean zero and variance oe 2 j . We wish to estimate -- the number of segments, C + 1, -- the segment boundaries, fv 1 ; : :...
