Analyzing and forecasting financial series with singular spectral analysis

Makshanov Andrey (), Musaev Alexander () and Grigoriev Dmitry ()
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Makshanov Andrey: Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping of Saint-Petersburg, 198035, St. Petersburg, Russia
Musaev Alexander: St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Saint-Petersburg State Institute of Technology, 190013, St. Petersburg, Russia
Grigoriev Dmitry: Saint-Petersburg State University, Center for Econometrics and Business Analytics (CEBA), 199034, St. Petersburg, Russia

Dependence Modeling, 2022, vol. 10, issue 1, 215-224

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: multidimensional chaotic processes; forecasting; singular spectrum analysis; immunocomputing; Forex (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:10:y:2022:i:1:p:215-224:n:3

DOI: 10.1515/demo-2022-0112

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