Hybrid mathematical models and methods of time series forecasting taking into account external factors

Authors

  • Виталий Николаевич Щелкалин Kharkіv National University of Radioelectronics 14, Lenina str., Kharkiv, Ukraine, 61166, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2014.31729

Keywords:

forecasting, structural identification, decomposition model, Box-Jenkins method, "Caterpillar"-SSA method

Abstract

The paper presents mathematical models of non-stationary time series forecasting, taking into account external factors and structural identification methods based on the joint use of a multidimensional variant of the "Caterpillar"-SSA method and SARIMAX models, expanded for time series forecasting with several seasonal components and taking into account several exogenous variables.

Structural and parametric identification of the transfer function, connecting the forecast and exogenous time series is performed in the proposed hybrid mathematical models, using formulas for L- or K-continuation of a multi-dimensional variant of the "Caterpillar"-SSA method. Decomposition approach to time series forecasting based on multi-dimensional variant of the "Caterpillar"-SSA method and SARIMAX models lies in decomposition of initial forecast and exogenous time series by the "Caterpillar"-SSA method into multiple time series with simpler structures; forecasting decomposition component data from SARIMAX models and calculating the total forecast, combining forecasts of the constructed simplified models.

The proposed models were tested on electricity and natural gas consumption time series, and the results of their forecasting were compared with the results, obtained by probabilistic SARIMAX models. Experimental results show the high efficiency of the proposed forecasting models in selecting suitable structural parameters in comparison with SARIMAX models.

The results lead to the conclusion that for effective forecasting, it is necessary to perform the decomposition of the studied time series and combine various models, describing both statistical and deterministic components of the time series, which provides better forecasting quality.

Author Biography

Виталий Николаевич Щелкалин, Kharkіv National University of Radioelectronics 14, Lenina str., Kharkiv, Ukraine, 61166

Assistant of Department of Applied Mathematics

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Published

2014-12-22

How to Cite

Щелкалин, В. Н. (2014). Hybrid mathematical models and methods of time series forecasting taking into account external factors. Eastern-European Journal of Enterprise Technologies, 6(4(72), 38–58. https://doi.org/10.15587/1729-4061.2014.31729

Issue

Vol. 6 No. 4(72) (2014): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2014 Виталий Николаевич Щелкалин

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