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Forecasting with the Fokker–Planck model: Bayesian setting of parameter

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  • Montagnon, Chris

Abstract

Using a closed solution to a Fokker–Planck model of a time series, a probability distribution for the next point in the time series is developed. This probability distribution has one free parameter. Various Bayesian approaches to setting this parameter are tested by forecasting some real world time series. Results show a more than 25% reduction in the ‘95% point’ of the probability distribution (the safety stock required in these real world situations), versus the conventional ARMA approach, without a significant increase in actuals exceeding this level.

Suggested Citation

  • Montagnon, Chris, 2017. "Forecasting with the Fokker–Planck model: Bayesian setting of parameter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 253-262.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:253-262
    DOI: 10.1016/j.physa.2016.12.005
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    References listed on IDEAS

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    1. Czechowski, Zbigniew & Telesca, Luciano, 2013. "Construction of a Langevin model from time series with a periodical correlation function: Application to wind speed data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5592-5603.
    2. Medino, Ary V. & Lopes, Sílvia R.C. & Morgado, Rafael & Dorea, Chang C.Y., 2012. "Generalized Langevin equation driven by Lévy processes: A probabilistic, numerical and time series based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 572-581.
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