This article is cited in 5 papers

Intellectual Control Systems, Data Analysis

Randomized machine learning of nonlinear models with application to forecasting the development of an epidemic process

A. Yu. Popkov

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, 119333 Russia

Abstract: We develop a discrete approach in the theory of randomized machine learning that is aimed at application to nonlinear models. We formulate the problem of entropy estimation of probability distributions and measurement noise for discrete nonlinear models. Issues related to the application of such models to forecasting problems, in particular, the problem of generating entropy-optimal distributions, are considered. The proposed methods are demonstrated on the solution of the problem of forecasting the total number of persons infected with novel coronavirus SARS-CoV-2 in Germany in 2020.

Keywords: randomized machine learning, entropy, entropy-based estimation, forecasting, randomized forecasting, COVID-19, SARS-CoV-2.

Presented by the member of Editorial Board: A. I. Mikhal'skii

Received: 15.10.2020
Revised: 12.01.2020
Accepted: 15.01.2021

DOI: 10.31857/S0005231021060064

 English version: Automation and Remote Control, 2021, 82:6, 1049–1064

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