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Financial time series prediction with the technology of complex Markov chains
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| dc.contributor.author | Соловйов, Володимир Миколайович | |
| dc.contributor.author | Saptsin, V. | |
| dc.contributor.author | Chabanenko, D. | |
| dc.date.accessioned | 2017-09-01T18:39:21Z | |
| dc.date.available | 2017-09-01T18:39:21Z | |
| dc.date.issued | 2014 | |
| dc.identifier.citation | Soloviev V. N. Financial time series prediction with the technology of complex Markov chains / V. Soloviev, V. Saptsin, D. Chabanenko // Моделювання та інформаційні технології в економіці : монографія / за заг. ред. В. М. Соловйова. – Черкаси, 2014. – С. 62-71. | uk |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/handle/0564/1305 | |
| dc.identifier.uri | https://doi.org/10.31812/0564/1305 | |
| dc.description | 1. Tikhonov V.I., Mironov V.A. Markov processes. - Moscow.: Soviet Radio, 1977. - 488 p. 2. Saptsin V., Experience of application genetically complex Markov chains for neural networks technology prediction, Visnyk Krivoriz`kogo ekonomichnogo institutu KNEU, Kriviy Rig, 2(18), 56- 66 (2009). 3. Saptsin V., Soloviev V. Relativistic quantum econophysics – new paradigms in complex systems modeling // arXiv:physics/0907.1142 [physics.soc-ph],7 Jul 2009. 4. Bookinham M. Noizes in electronic devices and systems. - Moscow., Mir, 1986. 5. Wasserman P. D., Neural computing: theory and practice (Van Nostrand Reinhold, New York, 1989). 6. Surovcev I. S., Klyukin V. I. and Pivovarova R. P., Neural networks (VGU, Voronezh, 1994) [in Russian]. 7. Ezhov A. A. and Shumskiy S. A., Neurocomputing and his applications in an economy and business (MIFI, Moscow, 1998) [in Russian]. 8. Mandelbrot B., The fractal geometry of nature (Freeman, San Francisco, 1982). 9. Rabiner R L (1989), A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, Vol. 77(2), pp. 257-286. 10.Weigend A. S., Gershenfeld N.A. Time Series Prediction: Forecasting the Future and Understanding the past. Addison-Wesley, 1993. 11.Zhang Y. «Prediction of Financial Time Series with Hidden Markov Models», in School of Computer Science, vol. Master of Applied Science: Simon Fraser University, 2004 12.Soloviev V., Saptsin V. and Chabanenko D. Prediction of financial time series with the technology of high-order Markov chains, Working Group on Physics of Socio-economic Systems (AGSOE).-Drezden, 2009, URL http://www.dpg-verhandlungen.de/2009/dresden/agsoe.pdf Appendix. | |
| dc.description.abstract | In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov Chains and simple first-ord yer ones is the existing of aftereffect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the «generalized state» as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. | uk |
| dc.language.iso | en | uk |
| dc.publisher | Брама-Україна | uk |
| dc.subject | complex Markov chains | uk |
| dc.subject | financial time-series | uk |
| dc.subject | price returns | uk |
| dc.title | Financial time series prediction with the technology of complex Markov chains | uk |
| dc.type | Article | uk |
