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Bayesian Approaches to Segmenting A Simple Time Series

Author

Listed:
  • Oliver, J.J.
  • Forbes, C.S.

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.

Suggested Citation

  • Oliver, J.J. & Forbes, C.S., 1997. "Bayesian Approaches to Segmenting A Simple Time Series," Monash Econometrics and Business Statistics Working Papers 14/97, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:1997-14
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    Cited by:

    1. Brian Hanlon & Catherine Forbes, 2002. "Model Selection Criteria for Segmented Time Series from a Bayesian Approach to Information Compression," Monash Econometrics and Business Statistics Working Papers 8/02, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Keywords

    ECONOMETRICS ; TIME SERIES;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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