Geodesic
Sample distribution function construction for non-stationary time-series forecasting
Matematičeskoe modelirovanie, Tome 26 (2014) no. 3, pp. 97-107 Cet article a éte moissonné depuis la source Math-Net.Ru

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The method of non-stationary time-series trajectory generation is proposed in accordance with Liouville evolution equation for the empirical distribution function density. With this generated series the non-autonomic dynamical chaotic system can be associated. For this system the Liapunov factor is estimated in the case of quasi-stationary initial distribution.
Keywords: non-stationary time series, empirical distribution function, kinetic evolution equation, Lyapunov exponent. 
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A. D. Bosov; R. S. Kalmetiev; Yu. N. Orlov. Sample distribution function construction for non-stationary time-series forecasting. Matematičeskoe modelirovanie, Tome 26 (2014) no. 3, pp. 97-107. http://geodesic.mathdoc.fr/item/MM_2014_26_3_a6/

[1] Feller V., Vvedenie v teoriyu veroyatnostei i ee prilozheniya, Mir, M., 1964 | Zbl

[2] Khardle V., Prikladnaya neparametricheskaya regressiya, Mir, M., 1993 | MR

[3] S. A. Prokhorov, Matematicheskoe opisanie i modelirovanie sluchainykh protsessov, Samarskii nauchnyi tsentr RAN, Uralsk, 2001

[4] B. L. S. Pracasa Rao, Statistical Inference for Fractional Diffusion Processes, Wiley, 2010, 249 pp. | MR

[5] Gnedenko B. V., Kurs teorii veroyatnostei, Fizmatlit, M., 1961, 406 pp. | MR

[6] http://www.finam.ru

[7] Yu. N. Orlov, K. P. Osminin, “Postroenie vyborochnoi funktsii raspredeleniya dlya prognozirovaniya nestatsionarnogo vremennogo ryada”, Matematich. modelirovanie, 2008, no. 9, 23–33 | MR | Zbl

[8] A. D. Bosov, Yu. N. Orlov, “Kinetiko-gidrodinamicheskii podkhod k prognozirovaniyu nestatsionarnykh vremennykh ryadov na osnove uravneniya Fokkera–Planka”, Tr. MFTI, 3:4 (2012)

[9] V. M. Anikin, A. F. Golubentsev, Analiticheskie modeli determinirovannogo khaosa, Fizmatlit, M., 2007, 328 pp.

[10] G. G. Malinetskii, Khaos. Struktury. Vychislitelnyi eksperiment. Vvedenie v nelineinuyu dinamiku, Editorial URSS, M., 2005