Investigating errors when forecasting processes with uncertain dynamics and observation noise by the self-adjusting brown's zero-order model

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.244623

Keywords:

forecasting errors, self-adjusting Brown's zero-order model, process dynamics uncertainty

Abstract

This paper reports a study into the errors of process forecasting under the conditions of uncertainty in the dynamics and observation noise using a self-adjusting Brown's zero-order model. The dynamics test models have been built for predicted processes and observation noises, which make it possible to investigate forecasting errors for the self-adjusting and adaptive models. The test process dynamics were determined in the form of a rectangular video pulse with a fixed unit amplitude, a radio pulse of the harmonic process with an amplitude attenuated exponentially, as well as a video pulse with amplitude increasing exponentially. As a model of observation noise, an additive discrete Gaussian process with zero mean and variable value of the mean square deviation was considered. It was established that for small values of the mean square deviation of observation noise, a self-adjusting model under the conditions of dynamics uncertainty produces a smaller error in the process forecast. For the test jump-like dynamics of the process, the variance of the forecast error was less than 1 %. At the same time, for the adaptive model, with an adaptation parameter from the classical and beyond-the-limit sets, the variance of the error was about 20 % and 5 %, respectively. With significant observation noises, the variance of the error in the forecast of the test process dynamics for the self-adjusting and adaptive models with a parameter from the classical set was in the range from 1 % to 20 %. However, for the adaptive model, with a parameter from the beyond-the-limit set, the variance of the prediction error was close to 100 % for all test models. It was established that with an increase in the mean square deviation of observation noise, there is greater masking of the predicted test process dynamics, leading to an increase in the variance of the forecast error when using a self-adjusting model. This is the price for predicting processes with uncertain dynamics and observation noises.

Author Biographies

Boris Pospelov, Scientific-Methodical Center of Educational Institutions in the Sphere of Civil Defence

Doctor of Technical Sciences, Professor

Evgenіy Rybka, National University of Civil Defence of Ukraine

Doctor of Technical Sciences, Senior Researcher

Research Center

Mikhail Samoilov, National University of Civil Defence of Ukraine

Adjunct

Research Center

Olekcii Krainiukov, V. N. Karazin Kharkiv National University

Doctor of Geographical Sciences, Professor

Department of Ecological Safety and Environmental Education

Yurii Kulbachko, Oles Honchar Dnipro National University

Doctor of Biology Sciences, Professor

Department of Ecology

Yuliia Bezuhla, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Prevention Activities and Monitoring

Oleksii Roianov, National University of Civil Defence of Ukraine

PhD

Department of Fire and Technological Safety of Facilities and Technologies

Svitlana Hryshko, Bogdan Khmelnitsky Melitopol State Pedagogical University

PhD, Associate Professor

Department of Physical Geography and Geology

Ivetta Krivitska, V. N. Karazin Kharkiv National University

PhD

Department of Ecological Safety and Environmental Education

Valentyna Ivanova, Melitopol State Pedagogical University

Senior Lecturer

Department of Physical Geography and Geology

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Published

2021-12-29

How to Cite

Pospelov, B., Rybka, E., Samoilov, M., Krainiukov, O., Kulbachko, Y., Bezuhla, Y., Roianov, O., Hryshko, S., Krivitska, I., & Ivanova, V. (2021). Investigating errors when forecasting processes with uncertain dynamics and observation noise by the self-adjusting brown’s zero-order model. Eastern-European Journal of Enterprise Technologies, 6(9 (114), 47–53. https://doi.org/10.15587/1729-4061.2021.244623

Issue

Vol. 6 No. 9 (114) (2021): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2021 Boris Pospelov, Evgenіy Rybka, Mikhail Samoilov, Olekcii Krainiukov, Yurii Kulbachko, Yuliia Bezuhla, Oleksii Roianov, Svitlana Hryshko, Ivetta Krivitska, Valentyna Ivanova

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