Affiliation:
1. Research Institute for Applied Problems of Mathematics and Informatics of the Belarusian State University
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.
Publisher
Publishing House Belorusskaya Nauka
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2 articles.
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