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Prediction of chaotic dynamical processes based on detection of regular component

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Abstract

The problems of detecting components and predicting dynamical processes are considered. Schemes for predicting chaotic time series that are based on detecting their regular, anomalous, and chaotic components followed by applying one of the described prediction methods to the regular component are presented. Regular components are detected using robust linear splines and singular spectrum analysis. Provided examples show that the presented schemes allow predicting dynamical processes with acceptable accuracy.

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References

  1. N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques (Chapman and Hall/CRC, London, 2001).

    Book  Google Scholar 

  2. A. V. Kryanev and G. V. Lukin, Mathematical Methods for Uncertain Data Processing (Fizmatlit, Moscow, 2006) [in Russian].

    MATH  Google Scholar 

  3. A. V. Kryanev, G. V. Lukin, and D. K. Udumyan, Metric Analysis and Data Processing (Fizmatlit, Moscow, 2012) [in Russian].

    Google Scholar 

  4. P. J. Huber, Robust Statistics (Wiley, New York, 1981; Mir, Moscow, 1983).

    Book  MATH  Google Scholar 

  5. F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust Statistics: The Approach Based on Influence Functions (Wiley, New York, 1985).

    Google Scholar 

  6. V. Ya. Arsenin, A. V. Kryanev, and M. V. Tsupko-Sitnikov, “Application of robust methods for ill-posed problems solving,” USSR Comput. Math. Math. Phys. 29(5), 653–661 (1989).

    MATH  MathSciNet  Google Scholar 

  7. I. Antoniou, P. Akritas, D. A. Burak, V. V. Ivanov, A. V. Kryanev, and G. V. Lukin, “Robust methods for stock market data analysis,” Physica A 336, 538–548 (2004).

    Article  MathSciNet  Google Scholar 

  8. I. Antoniou, P. Akritas, D. A. Burak, V. V. Ivanov, A. V. Kryanev, and G. V. Lukin, “Robust singular-spectrum analysis of stock market data,” Physica A 337, 334–345 (2004).

    MathSciNet  Google Scholar 

  9. A. V. Kryanev, V. V. Ivanov, G. V. Lukin, D. K. Udumyan, and S. G. Klimanov, “Mathematical methods and algorithms for predicting time processes based on the detection of deterministic components,” Vestn. Nats. Issled. Yadern. Univ. “MIFI” 2(2), 176–182 (2013).

    Google Scholar 

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Correspondence to V. V. Ivanov.

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Original Russian Text © V.V. Ivanov, S.G. Klimanov, A.V. Kryanev, G.V. Lukin, D.K. Udumyan, 2015, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2015, Vol. 55, No. 2, pp. 345–352.

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Ivanov, V.V., Klimanov, S.G., Kryanev, A.V. et al. Prediction of chaotic dynamical processes based on detection of regular component. Comput. Math. and Math. Phys. 55, 340–347 (2015). https://doi.org/10.1134/S0965542515020116

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  • DOI: https://doi.org/10.1134/S0965542515020116

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