АЛГОРИТМЫ ИССЛЕДОВАНИЯ МНОГОМЕРНЫХ ВРЕМЕННЫХ РЯДОВ С УЧЕТОМ ОТСРОЧЕННОГО ВЛИЯНИЯ ФАКТОРОВ НА ОСНОВЕ МАТЕМАТИЧЕСКОГО МОДЕЛИРОВАНИЯ
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

ALGORITHMS FOR RESEARCH OF MULTIDIMENSIONAL TIME SERIES TAKING INTO ACCOUNT THE EXTENDED INFLUENCE OF FACTORS ON THE BASIS OF MATHEMATICAL MODELING

Kryuchkova I.N.   Krasnovsky E.E.   Bolnokina evgenia vitalievna B.B.   Kravets O.Y.  

UDC 681.3
DOI: 10.26102/2310-6018/2018.23.4.008

  • Abstract
  • List of references
  • About authors

The models and methods of neural network modeling of dynamics based on the analysis of multidimensional time series, taking into account the delayed influence of significant factors, are investigated. In connection with the impossibility of simultaneous determination of the optimal time lag and network training, it is necessary to consider finding a multidimensional lag as a separate optimization problem. The mathematical formulation of the problem of building a neural network for a non-zero delay is described, a description of the optimization characteristics of the latency for one independent variable (input) is given, the information base for modeling and forecasting and neural network data processing algorithms are specified, and the latency vector for significant factors is optimized. The fundamental possibility of using sensitivity analysis to find the optimal multidimensional time lag was confirmed during the computational experiment. The sensitivity analysis was carried out on test data obtained by calculating the values of the sets of functions of several variables with a known delay for some variables. Analysis of learning errors, generalization and forecasting on the original and offset series allowed to conclude that there was a significant decrease in the training error and prediction error on the shifted series with a practically unchanged generalization error, which indicates the effectiveness of the proposed algorithm and the absence of structural effects in changing the quality of the forecast.

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Kryuchkova Irina Nikolaevna
Candidate of Technical Sciences Associate Professor

Voronezh State Technical University

Voronezh, Russian Federation

Krasnovsky Evgeny Efimovich
Candidate of Technical Sciences Associate Professor

Moscow State Technical University named after NE Bauman

Moscow, Russian Federation

Bolnokina evgenia vitalievna Bolnokina evgenia vitalievna Bolnokina evgenia vitalievna

Voronezh State Technical University

Voronezh, Russian Federation

Kravets Oleg Yakovlevich
Doctor of Technical Sciences Professor
Email: csit@bk.ru

Voronezh State Technical University

Voronezh, Russian Federation

Keywords: mathematical modeling, neural networks, lag, forecast

For citation: Kryuchkova I.N. Krasnovsky E.E. Bolnokina evgenia vitalievna B.B. Kravets O.Y. ALGORITHMS FOR RESEARCH OF MULTIDIMENSIONAL TIME SERIES TAKING INTO ACCOUNT THE EXTENDED INFLUENCE OF FACTORS ON THE BASIS OF MATHEMATICAL MODELING. Modeling, Optimization and Information Technology. 2018;6(4). Available from: https://moit.vivt.ru/wp-content/uploads/2018/10/KryuchkovaSoavtori_4_18_1.pdf DOI: 10.26102/2310-6018/2018.23.4.008 (In Russ).

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