Development of adaptive combined models for predicting time series based on similarity identification

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.121620

Keywords:

prediction of time series, search for similarities, adaptive combined model, Hurst index

Abstract

Adaptive combined models of hybrid and selective types for prediction of time series on the basis of a program set of adaptive polynomial models of various orders were offered. Selection in these models is carried out according to B-, R-, P-criteria with automatic formation of the basic set of models based on the adaptive D-criterion. It was found that these models had the maximum accuracy in the case of short-term and medium-term prediction of time series.

Adaptive combined selective prediction models based on the R- and B-criteria of selection with identification of similarities in the retrospection of time series by the nearest neighbor method was proposed. An adaptive combined hybrid model of prediction with identification of similarities in the retrospection of time series was constructed. It was found that these models had the highest accuracy in the case of medium-term prediction of time series.

Estimation of the prediction efficiency of various combined models depending on the level of persistency of time series was made. It has been found that in the case of short-term prediction for the prediction period τ≤2, the adaptive combined hybrid prediction model is the most accurate. Selective models with various selection criteria are effective in predicting persistent time series with the Hurst index H>0.75 for the prediction period τ>2. In the case of prediction of time series with the Hurst index for the prediction period τ>2, the adaptive combined hybrid and selective models with identification of similarities in the retrospection of the time series are more precise.

Author Biographies

Alexander Kuchansky, Kyiv National University of Construction and Architecture Povitroflotskyi ave., 31, Kyiv, Ukraine, 03037

PhD, Associate Professor

Department of Cybersecurity and Computer Engineering

Andrii Biloshchytskyi, Taras Shevchenko National University of Kyiv Volodymyrska str., 60, Kyiv, Ukraine, 01033

Doctor of Technical Sciences, Professor, Head of Department

Department of Information Systems and Technologies

Yurii Andrashko, Uzhhorod National University Narodna sq., 3, Uzhhorod, Ukraine, 88000

Lecturer

Department of System Analysis and Optimization Theory 

Svitlana Biloshchytska, Kyiv National University of Construction and Architecture Povitroflotskyi ave., 31, Kyiv, Ukraine, 03037

PhD, Associate Professor

Department of Information Technology Designing and Applied Mathematics

Yevheniia Shabala, Kyiv National University of Construction and Architecture Povitroflotskyi ave., 31, Kyiv, Ukraine, 03037

PhD, Associate Professor

Department of Cybersecurity and Computer Engineering

Oleksii Myronov, Kyiv National University of Construction and Architecture Povitroflotskyi ave., 31, Kyiv, Ukraine, 03037

Postgraduate student

Department of Information Technologies

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Published

2018-01-24

How to Cite

Kuchansky, A., Biloshchytskyi, A., Andrashko, Y., Biloshchytska, S., Shabala, Y., & Myronov, O. (2018). Development of adaptive combined models for predicting time series based on similarity identification. Eastern-European Journal of Enterprise Technologies, 1(4 (91), 32–42. https://doi.org/10.15587/1729-4061.2018.121620

Issue

Vol. 1 No. 4 (91) (2018): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2018 Alexander Kuchansky, Andrii Biloshchytskyi, Yurii Andrashko, Svitlana Biloshchytska, Yevheniia Shabala, Oleksii Myronov

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