Forecast of consumer behaviour based on neural networks models comparison

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 en 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 feedforward 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.