Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Analyzing and forecasting financial series with singular spectral analysis. / Макшанов, Андрей; Мусаев, Александр Азерович; Григорьев, Дмитрий Алексеевич.
в: Dependence Modeling, Том 10, № 1, 2022, стр. 215-224.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Analyzing and forecasting financial series with singular spectral analysis
AU - Макшанов, Андрей
AU - Мусаев, Александр Азерович
AU - Григорьев, Дмитрий Алексеевич
N1 - Publisher Copyright: © 2022 Andrey Makshanov et al., published by De Gruyter.
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