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Forecasting Multivariate Chaotic Processes with Precedent Analysis

1
Saint-Petersburg State Institute of Technology, Technical University, St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 190013 St. Petersburg, Russia
2
Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping, 198035 St. Petersburg, Russia
3
Center for Econometrics and Business Analytics, Saint-Petersburg State University, 199034 St. Petersburg, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Demos T. Tsahalis
Computation 2021, 9(10), 110; https://doi.org/10.3390/computation9100110
Received: 4 September 2021 / Revised: 11 October 2021 / Accepted: 13 October 2021 / Published: 19 October 2021
(This article belongs to the Section Computational Engineering)
Predicting the state of a dynamic system influenced by a chaotic immersion environment is an extremely difficult task, in which the direct use of statistical extrapolation computational schemes is infeasible. This paper considers a version of precedent forecasting in which we use the aftereffects of retrospective observation segments that are similar to the current situation as a forecast. Furthermore, we employ the presence of relatively stable correlations between the parameters of the immersion environment as a regularizing factor. We pay special attention to the choice of similarity measures or distances used to find analog windows in arrays of retrospective multidimensional observations. View Full-Text
Keywords: stochastic process forecasting; precedent analysis; multidimensional observations stochastic process forecasting; precedent analysis; multidimensional observations
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MDPI and ACS Style

Musaev, A.; Makshanov, A.; Grigoriev, D. Forecasting Multivariate Chaotic Processes with Precedent Analysis. Computation 2021, 9, 110. https://doi.org/10.3390/computation9100110

AMA Style

Musaev A, Makshanov A, Grigoriev D. Forecasting Multivariate Chaotic Processes with Precedent Analysis. Computation. 2021; 9(10):110. https://doi.org/10.3390/computation9100110

Chicago/Turabian Style

Musaev, Alexander, Andrey Makshanov, and Dmitry Grigoriev. 2021. "Forecasting Multivariate Chaotic Processes with Precedent Analysis" Computation 9, no. 10: 110. https://doi.org/10.3390/computation9100110

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