Artificial Neural Networks Numerical Forecasting of Economic Time Series

Abstract

The current global market is driven by many factors, e.g. by the facts that we live in the information age and that information is distributed in short times, large amounts and by many data channels. It is practically impossible to analyse all kinds of incoming information flows and transform them to data by classical methods. New requirements call for new methods. Artificial neural networks once trained on patterns can be used for forecasting and they are able to work with extremely big datasets in reasonable time. Traditionally, this is solved by means of a statistical analysis first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The common point for both methods is the learning process from samples of past data, or learning from the past. From many of the uncommon points the input conditions for the model creation and the length of the time series pattern set could be pointed out. On one hand, very sophisticated statistical methods exist that have strictly defined input conditions for datasets; on the other hand, practically open input conditions of artificial neural networks can be used. Regarding the length of the time series, the main problem of the Czech Republic, short and middle term predictions are valuable datasets. The lengths of selected economic values are not huge enough for quality of prediction or forecasting. Hand-in-hand with typical problems of real datasets (noisiness and/or missing data), there is the issue of the quality of the numerical forecasting. In addition, the strong nonlinearity of the models leads to an unsolvable usage of classical methods or construction of models that are not representing the reality. These are only few of the difficulties related to economic and financial modelling and prediction. Possible problems of numerous types of the artificial neural networks with n-setups make the issue even more complicated. The aim of this chapter is to compare different types of artificial neural networks using short and middle terms predictions of a real-world economic index. A number of papers dealing with artificial neural networks used for particular problems and often for the test do not use real-world economic indexes. The chapter is divided into four sections. The first simply presents the introduction to the research domain. The second section describes state-of-the-art artificial intelligence approaches to both prediction and forecasting of economic indexes. In the third section, neural network types and learning algorithms dealing with the prediction of time series and learning optimization are presented. In detail, the third section also includes methods of verification and validation of artificial neural networks and description of real-world economic indexes