Analyzing and forecasting financial series with singular spectral analysis

Dependence Modeling

Makshanov Andrey¹  Grigoriev Dmitry²  Musaev Alexander³ 

[1] Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping of Saint-Petersburg, 198035, St. Petersburg, Russia;Saint-Petersburg State University, Center for Econometrics and Business Analytics (CEBA), 199034, St. Petersburg, Russia;St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Saint-Petersburg State Institute of Technology, 190013, St. Petersburg, Russia;

关键词: multidimensional chaotic processes;    forecasting;    singular spectrum analysis;    immunocomputing;    forex;    37m20;    37m10;    90c90;   

DOI  :  10.1515/demo-2022-0112

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown