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A Bayesian Approach to Event Prediction

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  • M. Antunes
  • M. A. Amaral Turkman
  • K. F. Turkman

Abstract

. In a series of papers, Lindgren (1975a, 1985) and de Maré (1980) set the principles of optimal alarm systems and obtained the basic results. Application of these ideas to linear discrete time‐series models was carried out by Svensson et al. (1996). In this paper, we suggest a Bayesian predictive approach to event prediction and optimal alarm systems for discrete time series. There are two novelties in this approach: first, the variation in the model parameters is incorporated in the analysis; second, this method allows ‘on‐line prediction’ in the sense that, as we observe the process, our posterior probabilities and predictions are updated at each time point.

Suggested Citation

  • M. Antunes & M. A. Amaral Turkman & K. F. Turkman, 2003. "A Bayesian Approach to Event Prediction," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(6), pages 631-646, November.
    • Handle: RePEc:bla:jtsera:v:24:y:2003:i:6:p:631-646
    DOI: 10.1111/j.1467-9892.2003.00326.x
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    References listed on IDEAS

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    1. A. Svensson & J. Holst & R. Lindquist & G. Lindgren, 1996. "Optimal Prediction Of Catastrophes In Autoregressive Moving‐Average Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(5), pages 511-531, September.
    2. Turkman, M. A. Amaral & Turkman, K. F., 1990. "Optimal alarm systems for autoregressive processes : A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 10(3), pages 307-314, December.
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    Cited by:

    1. Pedregal, Diego J. & Carmen Carnero, Ma, 2006. "State space models for condition monitoring: a case study," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 171-180.
    2. Pasquale Cirillo & Jürg Hüsler & Pietro Muliere, 2013. "Alarm Systems and Catastrophes from a Diverse Point of View," Methodology and Computing in Applied Probability, Springer, vol. 15(4), pages 821-839, December.
    3. M. de Carvalho & K. F. Turkman & A. Rua, 2013. "Dynamic threshold modelling and the US business cycle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 535-550, August.

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