Control Systems and Computers, N4, 2016, Article 9


Upr. sist. maš., 2016, Issue 4 (264), pp. 80-85.

UDC 519.163 + 681.5.015

Yefimenko S.M., International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine,

System Modeling and Prediction of the Multidimensional Interrelated Processes

Introduction. The problem of the mathematical modelling and prediction of the multidimensional interrelated time series is considered. It is used in economy, ecology and sociology. While many scientific proceedings are dedicated to modelling of one-dimensional time series, the experience of multidimensional time series modelling is insufficient.

Methods. An approach to the structural and parameters identification of the multidimensional time series is considered when parameters for every model is estimated independently. An algorithm with selecting of more than one best model for every process is used. The purpose is to combine all possible variants of system models and to select the best one by additional criterion.

Results. Theoretical grounds of recurrent-and-parallel computing in combinatorial GMDH algorithm and software for modeling and prediction of complex multidimensional interrelated processes in the class of vector autoregression models are developed.

Conclusion. The scheme of paralleling for recurrent COMBI algorithm allows to solve the problem when arguments amount exceeds capability of scheme with the exhaustive search. The effectiveness of the constructed algorithm is demonstrated by prediction of the interrelated processes in the field of investment activity of Ukraine with the purpose of information support of administrative decisions.

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KeywordsMultidimensional Interrelated Processes, System Modeling, prediction, structural and parameters identification.

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