Neural network-based models of binomial time series in data analysis problems
https://doi.org/10.29235/1561-8323-2021-65-6-654-660
Abstract
This article is devoted to constructing neural network-based models for discrete-valued time series and their use in computer data analysis. A new family of binomial time series based on neural networks is presented, which makes it possible to approximate the arbitrary-type stochastic dependence in time series. Ergodicity conditions and an equivalence relation for these models are determined. Consistent statistical estimators for model parameters and algorithms for computer data analysis (including forecasting and pattern recognition) are developed.
About the Author
Yu. S. Kharin
Research Institute for Applied Problems of Mathematics and Informatics of the Belarusian State University
Belarus
Belarus
Kharin Yuriy S. – Correspondent Member, D. Sc. (Physics and Mathematics), Professor, Director
4, Nezavisimosti Ave., Minsk, 220030, Republic of Belarus
References
1. Kellenher J. D., Tiernay B. Data Science. New York, 2021. 280 p.
2. Fan J., Li R., Zhang C. H., Zou H. Statistical foundations of Data Science. New York, 2021. 729 p. https://doi.org/10.1201/9780429096280
3. Fokianos K., Fried R., Kharin Yu., Voloshko V. Statistical analysis of multivariate discrete-valued time series. Journal of Multivariate Analysis, 2021, vol. 186, art. 104805. 15 p. https://doi.org/10.1016/j.jmva.2021.104805
4. Kharin Yu., Voloshko V. Robust estimation for Binomial conditionally nonlinear autoregressive time series based on multivariate conditional frequencies. Journal of Multivariate Analysis, 2021, vol. 185, art. 104777, pp. 11–27. https://doi.org/10.1016/j.jmva.2021.104777
5. Kolmogorov A. N. On representation of continuous functions od many variables by superposition of continuous functions of one variable and addition. Doklady Akademii Nauk SSSR = Doklady of the Academy of Sciences of SSSR, 1957, vol. 114, pp. 953–956 (in Russian).
6. Cybenko G. Approximation by superpositions of sigmoidal functions. Mathematics of Control, Signals, and Systems, 1929, vol. 2, no. 4, pp. 303–314. https://doi.org/10.1007/bf02551274
7. Kharin Yu. Robustness in Statistical Forecasting. Heidelberg, New York, Dordrecht, London, 2013. 356 p. https://doi.org/10.1007/978-3-319-00840-0
8. Kharin Yu. Robustness in Statistical Pattern Recognition. Dordrecht, Boston, London, 1996. 302 p. https://doi.org/10.1007/978-94-015-8630-6
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