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Analyzing and forecasting financial series with singular spectral analysis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Makshanov Andrey & Musaev Alexander & Grigoriev Dmitry, 2022. "Analyzing and forecasting financial series with singular spectral analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 215-224, January.
    • Handle: RePEc:vrs:demode:v:10:y:2022:i:1:p:215-224:n:3
    DOI: 10.1515/demo-2022-0112
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    References listed on IDEAS

    as
    1. Alexander Musaev & Dmitry Grigoriev, 2022. "Numerical Studies of Statistical Management Decisions in Conditions of Stochastic Chaos," Mathematics, MDPI, vol. 10(2), pages 1-14, January.
    2. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
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