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Назва: Markov chains applications to the financial-economic time series predictions
Автори: Соловйов, Володимир Миколайович
Saptsin, V.
Chabanenko, D.
Ключові слова: prediction
time series
complex Markov chains
discrete time
fractal properties
Дата публікації: 2011
Видавництво: Transport and Telecommunication Institute
Бібліографічний опис: Soloviev V. N. Markov chains applications to the financial-economic time series predictions / V. Soloviev, V. Saptsin, D. Chabanenko // Computer Modelling and New Technologies. – 2011. – Vol. 15, no. 3. – Pp. 16-20.
Короткий огляд (реферат): In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world’s stock market indices are presented.
Опис: 1. Samarskii, A. A. and A. P. Mikhailov. Mathematical Modeling: Ideas. Methods. Examples. Moscow: Fizmatlit, 2001. 2. Ivakhnenko, O. G. Grouping Method of Data Handling – the Concurrent of Stochastic Approximation Methods (in Ukrainian), Automatika , Vol. 3 (3), 1968, pp. 58–72. 3. Saptsin, V. and Soloviev, V. Relativistic Quantum Econophysics – New Paradigms in Complex Systems Modelling. arXiv:0907.1142v1 [physics.soc-ph]. 4. Von Bertalanffy, L. General Syst em Theory – a Critical Review, General Systems , VII, 1962, pp. 1–20. 5. Kurbanov, K. R. and V. M. Saptsin. Markov Chains as Technology for Social, Economic and Ecological Processes Forecasting. In: Problems of Regional Perspectives of the Market Economy, Kremenchuk, May, 11–13 2007 , pp. 10–14. (In Russian) 6. Saptsin, V. M. Experience of Using Genetically Complex Markov Chains for the Neural Network Technology Forecasting, Visnyk Krivorizkogo ekonomichnogo institutu KNEU , Vol. 2 (18), pp. 56–66, 2009. 7. Lukashin, Y. P. Adaptive Methods of Time Seri es Forecasting: Textbook. Moscow: Finance and Statistics, 2003. 8. Zaichenko, Y. P. Fuzzy Models and Techniques in Intelligent Systems: Monograph. Kiev: Slovo, 2008. (In Russian) 9. Ezhov, A. A. and S. A. Shumsky. Neurocomputing and its Application in Economics and Business (Series “Textbooks” of Economic-Analytical Institu te MEPI) / Ed. by Professor V. V. Kharitonov . Moscow: MEPI, 1998. 10. Soloviev, V., Saptsin, V. and D. Chabanenko. Financial time series prediction with the technology of complex Markov chains, Computer Modelling and New Technologies , Vol. 14 (3), 2010, pp. 63–67. http://www.tsi.lv/RSR/vol14 3/14 3-7.pdf 11. Soloviev, V., Saptsin, V. and D. Chabanenko. Markov Chains application to the financial-economic time series prediction . – arXiv:1111:5254, November 2011. 12. Tikhonov, V. I. and V. A. Mironov. Markov Processes . Moscow: Soviet Radio, 1977. 13. Raftery, Adrian E. A Model for High-Order Markov Chains, Journal of the Royal Statistical Society, 1985. 14. Raftery, Adrian and Simon Tavare. Estimation and M odelling Repeated Patterns in High Order Markov Chains with the Mixture Transition Distribution Model, Appl. Statist ., Vol. 43 (1), 1994, pp. 179–199. 15. Chabanenko, D. M. Discrete Fourie r-Based Forecasting of Time Series, Sistemni tehnologii. Regionalny mizhvuzivsky zbirnik naukovyh pratz (in Ukrainian), Vol. 1 (66), 2010, pp. 114–121. – http://www.nbuv.gov.ua/portal/n atural/syte/2010 1/15.pdf
URI (Уніфікований ідентифікатор ресурсу): http://elibrary.kdpu.edu.ua/handle/0564/1189
https://doi.org/10.31812/0564/1189
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

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