Analyzing and forecasting financial series with singular spectral analysis
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 determ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-06-01
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| Series: | Dependence Modeling |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/demo-2022-0112 |
| _version_ | 1797998392696111104 |
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| author | Makshanov Andrey Musaev Alexander Grigoriev Dmitry |
| author_facet | Makshanov Andrey Musaev Alexander Grigoriev Dmitry |
| author_sort | Makshanov Andrey |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:48:04Z |
| format | Article |
| id | doaj.art-c0ae399b93fd480c825bd9b441b94e93 |
| institution | Directory Open Access Journal |
| issn | 2300-2298 |
| language | English |
| last_indexed | 2024-04-11T10:48:04Z |
| publishDate | 2022-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Dependence Modeling |
| spelling | doaj.art-c0ae399b93fd480c825bd9b441b94e932022-12-22T04:28:59ZengDe GruyterDependence Modeling2300-22982022-06-0110121522410.1515/demo-2022-0112Analyzing and forecasting financial series with singular spectral analysisMakshanov Andrey0Musaev Alexander1Grigoriev Dmitry2Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping of Saint-Petersburg, 198035, St. Petersburg, RussiaSt. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Saint-Petersburg State Institute of Technology, 190013, St. Petersburg, RussiaSaint-Petersburg State University, Center for Econometrics and Business Analytics (CEBA), 199034, St. Petersburg, RussiaModern 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.https://doi.org/10.1515/demo-2022-0112multidimensional chaotic processesforecastingsingular spectrum analysisimmunocomputingforex37m2037m1090c90 |
| spellingShingle | Makshanov Andrey Musaev Alexander Grigoriev Dmitry Analyzing and forecasting financial series with singular spectral analysis Dependence Modeling multidimensional chaotic processes forecasting singular spectrum analysis immunocomputing forex 37m20 37m10 90c90 |
| title | Analyzing and forecasting financial series with singular spectral analysis |
| title_full | Analyzing and forecasting financial series with singular spectral analysis |
| title_fullStr | Analyzing and forecasting financial series with singular spectral analysis |
| title_full_unstemmed | Analyzing and forecasting financial series with singular spectral analysis |
| title_short | Analyzing and forecasting financial series with singular spectral analysis |
| title_sort | analyzing and forecasting financial series with singular spectral analysis |
| topic | multidimensional chaotic processes forecasting singular spectrum analysis immunocomputing forex 37m20 37m10 90c90 |
| url | https://doi.org/10.1515/demo-2022-0112 |
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