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New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction

Author

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  • Yuri S. Popkov

    (Institute for Systems Analysis of Russian Academy of Sciences, Moscow 117312, Russia
    Moscow Institute of Physics and Technology (State University), Moscow 141700, Russia
    National Research University Higher School of Economics, Moscow 101000, Russia)

  • Yuri A. Dubnov

    (Institute for Systems Analysis of Russian Academy of Sciences, Moscow 117312, Russia
    Moscow Institute of Physics and Technology (State University), Moscow 141700, Russia)

  • Alexey Yu. Popkov

    (Institute for Systems Analysis of Russian Academy of Sciences, Moscow 117312, Russia
    Moscow Institute of Physics and Technology (State University), Moscow 141700, Russia)

Abstract

We propose a new method of randomized forecasting (RF-method), which operates with models described by systems of linear ordinary differential equations with random parameters. The RF-method is based on entropy-robust estimation of the probability density functions (PDFs) of model parameters and measurement noises. The entropy-optimal estimator uses a limited amount of data. The method of randomized forecasting is applied to World population prediction. Ensembles of entropy-optimal prognostic trajectories of World population and their probability characteristics are generated. We show potential preferences of the proposed method in comparison with existing methods.

Suggested Citation

  • Yuri S. Popkov & Yuri A. Dubnov & Alexey Yu. Popkov, 2016. "New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction," Mathematics, MDPI, vol. 4(1), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:4:y:2016:i:1:p:16-:d:65623
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    References listed on IDEAS

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    Cited by:

    1. Yuri S. Popkov & Yuri A. Dubnov & Alexey Yu. Popkov, 2023. "Reinforcement Procedure for Randomized Machine Learning," Mathematics, MDPI, vol. 11(17), pages 1-14, August.

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