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Accelerated Maximum Entropy Method for Time Series Models Estimation

Author

Listed:
  • Yuri A. Dubnov

    (Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia
    Higher Schools of Economics, National Research University, 20 Myasnitskaya, 109028 Moscow, Russia)

  • Alexandr V. Boulytchev

    (Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 44/2 Vavilova, 119333 Moscow, Russia
    Higher Schools of Economics, National Research University, 20 Myasnitskaya, 109028 Moscow, Russia)

Abstract

The work is devoted to the development of a maximum entropy estimation method with soft randomization for restoring the parameters of probabilistic mathematical models from the available observations. Soft randomization refers to the technique of adding regularization to the functional of information entropy in order to simplify the optimization problem and speed up the learning process compared to the classical maximum entropy method. Entropic estimation makes it possible to restore probability distribution functions for model parameters without introducing additional assumptions about the likelihood function; thus, this estimation method can be used in problems with an unspecified type of measurement noise, such as analysis and forecasting of time series.

Suggested Citation

  • Yuri A. Dubnov & Alexandr V. Boulytchev, 2023. "Accelerated Maximum Entropy Method for Time Series Models Estimation," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:4000-:d:1244297
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    References listed on IDEAS

    as
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