Forecasting Multivariate Chaotic Processes with Precedent Analysis

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Forecasting Multivariate Chaotic Processes with Precedent Analysis. / Musaev, Alexander ; Makshanov, Andrey ; Grigoriev, Dmitry .

In: Computation, Vol. 9, No. 10, 110, 10.2021.

Research output: Contribution to journal › Article › peer-review

Author

Musaev, Alexander ; Makshanov, Andrey ; Grigoriev, Dmitry . / Forecasting Multivariate Chaotic Processes with Precedent Analysis. In: Computation. 2021 ; Vol. 9, No. 10.

BibTeX

@article{e7d3b76de29f4212b6259f01f8052ca6,

title = "Forecasting Multivariate Chaotic Processes with Precedent Analysis",

abstract = "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.",

keywords = "Multidimensional observations, Precedent analysis, Stochastic process forecasting",

author = "Alexander Musaev and Andrey Makshanov and Dmitry Grigoriev",

note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = oct,

doi = "10.3390/computation9100110",

language = "English",

volume = "9",

journal = "Computation",

issn = "2079-3197",

publisher = "MDPI AG",

number = "10",

}

RIS

TY - JOUR

T1 - Forecasting Multivariate Chaotic Processes with Precedent Analysis

AU - Musaev, Alexander

AU - Makshanov, Andrey

AU - Grigoriev, Dmitry

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

KW - Multidimensional observations

KW - Precedent analysis

KW - Stochastic process forecasting

UR - https://www.mdpi.com/2079-3197/9/10/110

UR - http://www.scopus.com/inward/record.url?scp=85118324312&partnerID=8YFLogxK

U2 - 10.3390/computation9100110

DO - 10.3390/computation9100110

M3 - Article

VL - 9

JO - Computation

JF - Computation

SN - 2079-3197

IS - 10

M1 - 110

ER -

ID: 87278712