Analyzing and forecasting financial series with singular spectral analysis

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Analyzing and forecasting financial series with singular spectral analysis. / Макшанов, Андрей; Мусаев, Александр Азерович; Григорьев, Дмитрий Алексеевич.

In: Dependence Modeling, Vol. 10, No. 1, 2022, p. 215-224.

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

Author

Макшанов, Андрей ; Мусаев, Александр Азерович ; Григорьев, Дмитрий Алексеевич. / Analyzing and forecasting financial series with singular spectral analysis. In: Dependence Modeling. 2022 ; Vol. 10, No. 1. pp. 215-224.

BibTeX

@article{f057b275624c414aa1baefd5f7f912b8,

title = "Analyzing and forecasting financial series with singular spectral analysis",

abstract = "Modern techniques for managing multidimensional stochastic processes that reflect the dynamics of unstable environments are proactive, which refers to decision making based on forecasting the system's state vector evolution. At the same time, the dynamics of open nonlinear systems are largely determined by their chaotic nature, which leads to a violation of stationarity and ergodicity of the series of observations and, as a result, to a catastrophic decrease in the efficiency of forecasting algorithms based on traditional methods of multivariate statistical data analysis. In this article, we make an attempt to reduce the instability influence by employing singular spectrum analysis (SSA) algorithms. This technique has been employed in a wide class of applied data analysis problems formulated in terms of singular decomposition of data matrices: technologies of immunocomputing and SSA.",

keywords = "Forex, forecasting, immunocomputing, multidimensional chaotic processes, singular spectrum analysis",

author = "Андрей Макшанов and Мусаев, {Александр Азерович} and Григорьев, {Дмитрий Алексеевич}",

note = "Publisher Copyright: {\textcopyright} 2022 Andrey Makshanov et al., published by De Gruyter.",

year = "2022",

doi = "10.1515/demo-2022-0112",

language = "English",

volume = "10",

pages = "215--224",

journal = "Dependence Modeling",

issn = "2300-2298",

publisher = "De Gruyter",

number = "1",

}

RIS

TY - JOUR

T1 - Analyzing and forecasting financial series with singular spectral analysis

AU - Макшанов, Андрей

AU - Мусаев, Александр Азерович

AU - Григорьев, Дмитрий Алексеевич

PY - 2022

Y1 - 2022

N2 - Modern techniques for managing multidimensional stochastic processes that reflect the dynamics of unstable environments are proactive, which refers to decision making based on forecasting the system's state vector evolution. At the same time, the dynamics of open nonlinear systems are largely determined by their chaotic nature, which leads to a violation of stationarity and ergodicity of the series of observations and, as a result, to a catastrophic decrease in the efficiency of forecasting algorithms based on traditional methods of multivariate statistical data analysis. In this article, we make an attempt to reduce the instability influence by employing singular spectrum analysis (SSA) algorithms. This technique has been employed in a wide class of applied data analysis problems formulated in terms of singular decomposition of data matrices: technologies of immunocomputing and SSA.

AB - Modern techniques for managing multidimensional stochastic processes that reflect the dynamics of unstable environments are proactive, which refers to decision making based on forecasting the system's state vector evolution. At the same time, the dynamics of open nonlinear systems are largely determined by their chaotic nature, which leads to a violation of stationarity and ergodicity of the series of observations and, as a result, to a catastrophic decrease in the efficiency of forecasting algorithms based on traditional methods of multivariate statistical data analysis. In this article, we make an attempt to reduce the instability influence by employing singular spectrum analysis (SSA) algorithms. This technique has been employed in a wide class of applied data analysis problems formulated in terms of singular decomposition of data matrices: technologies of immunocomputing and SSA.

KW - Forex

KW - forecasting

KW - immunocomputing

KW - multidimensional chaotic processes

KW - singular spectrum analysis

UR - http://www.scopus.com/inward/record.url?scp=85133640134&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/46f3380b-d4b2-36f6-8e1a-a944e0d4817f/

U2 - 10.1515/demo-2022-0112

DO - 10.1515/demo-2022-0112

M3 - Article

VL - 10

SP - 215

EP - 224

JO - Dependence Modeling

JF - Dependence Modeling

SN - 2300-2298

IS - 1

ER -

ID: 96663463