Forecast of consumer behaviour based on neural networks models comparison

Michael Štencl, Ondřej Popelka and Jiří Šťastný
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Michael Štencl: Ústav informatiky, Mendelova univerzita v Brně, 613 00 Brno, Česká republika
Ondřej Popelka: Ústav informatiky, Mendelova univerzita v Brně, 613 00 Brno, Česká republika
Jiří Šťastný: Ústav informatiky, Mendelova univerzita v Brně, 613 00 Brno, Česká republika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2012, vol. 60, issue 2, 437-442

Abstract: The aim of this article is comparison of accuracy level of forecasted values of several artificial neural network models. The comparison is performed on datasets of Czech household consumption values. Several statistical models often resolve this task with more or fewer restrictions. In previous work where models' input conditions were not so strict and model with missing data was used (the time series didn't contain many values) we have obtained comparably good results with artificial neural networks. Two views - practical and theoretical, motivate the purpose of this study. Forecasting models for medium term prognosis of the main trends of Czech household consumption is part of the faculty research design grant MSM 6215648904/03/02 (Sub-task 5.3) which defines the practical purpose. Testing of nonlinear autoregressive artificial neural network model compared with feed-forward neural network and radial basis function neural network defines the theoretical purpose. The performance metrics of the models were evaluated using a combination of common error metrics, namely Correlation Coefficient and Mean Square Error, together with the number of epochs and/or main prediction error.

Keywords: artificial neural networks; forecasting methods; customer behaviour (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:mup:actaun:actaun_2012060020437

DOI: 10.11118/actaun201260020437

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