Modifying the method for forecasting hazardous processes with unknown dynamics in the presence of noise

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.252076

Keywords:

modified forecasting method, unknown dynamics, masking noise, Brown’s model, forecasting errors

Abstract

This paper has substantiated a modified method that, within the framework of the adaptive zero-order Brown’s model, provides for increased accuracy in predicting processes with unknown dynamics masked by the noise of various levels. The forecasting method modification essentially involves an adaptive technique for determining the weight of the correction of the previous forecast, taking into consideration the recurrent state of the predicted process in time. To investigate the accuracy of the forecasting method, a test model of the process dynamics was determined in the form of a rectangular pulse with unit amplitude. In addition, a model of additive masking noise was defined in the form of a discrete Gaussian process with a zero mean and a variable value of the mean square deviation. Based on determining the exponentially smoothed values of current absolute forecasting errors, the dynamics of forecast accuracy were examined for the modified and self-adjusting methods. It was found that for the mean quadratic deviation of the masking noise equal to 0.9, the smoothed absolute prediction error for the modified method does not exceed 23 %; for the self-adjusting method – 42 %. This means that the prediction accuracy for the modified method is about twice as high. In the case of an average square deviation of masking noise of 0.1, the smoothed absolute prediction error for the modified and self-adjusting methods is approximately the same and does not exceed 10 %. That means that at a low level of masking noise, both prediction methods provide approximately the same accuracy. However, with an increase in the level of masking noise, the self-adjusting method significantly loses the accuracy of the forecast to the proposed modified method.

Author Biographies

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

Doctor of Technical Sciences, Professor

Vladimir Andronov, National University of Civil Defence of Ukraine

Doctor of Technical Sciences, Professor

Research Center

Olekcii Krainiukov, V. N. Karazin Kharkiv National University

Doctor of Geographical Sciences, Professor

Department of Environmental Safety and Environmental Education

Kostiantyn Karpets, National University of Civil Defence of Ukraine

PhD, Associate Professor

Research Center

Yuliia Bezuhla, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Prevention Activities and Monitoring

Kostiantyn Fisun, National Academy of the National Guard of Ukraine

Doctor of Economic Sciences, Professor

Department of Management and the Military Economy

Svyatoslav Manzhura, National Academy of the National Guard of Ukraine

PhD

Research Center

Svitlana Hryshko, Bogdan Khmelnitsky Melitopol State Pedagogical University

PhD, Associate Professor

Department of Physical Geography and Geology

Olga Mukhina, H. S. Skovoroda Kharkiv National Pedagogical University

PhD, Associate Professor

Research Center

Valentyna Ivanova, Bogdan Khmelnitsky Melitopol State Pedagogical University

Department of Physical Geography and Geology

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Published

2022-02-25

How to Cite

Pospelov, B., Andronov, V., Krainiukov, O., Karpets, K., Bezuhla, Y., Fisun, K., Manzhura, S., Hryshko, S., Mukhina, O., & Ivanova, V. (2022). Modifying the method for forecasting hazardous processes with unknown dynamics in the presence of noise. Eastern-European Journal of Enterprise Technologies, 1(4 (115), 29–36. https://doi.org/10.15587/1729-4061.2022.252076

Issue

Vol. 1 No. 4 (115) (2022): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2022 Boris Pospelov, Vladimir Andronov, Olekcii Krainiukov, Kostiantyn Karpets, Yuliia Bezuhla, Kostiantyn Fisun, Svyatoslav Manzhura, Svitlana Hryshko, Olga Mukhina, Valentyna Ivanova

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