Bayesian Approaches to Segmenting a Simple Time Series

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.


Issue Date:
Nov 01 1997
Publication Type:
Working or Discussion Paper
Language:
English
Total Pages:
22
Series Statement:
Working Paper 14/97




 Record created 2018-02-06, last modified 2018-02-07

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