Applications of neural networking models in forecasting methods

Abstract

Artificial neural networks are frequently used in business, engineering, and scientific applications. Their increasing popularity is due to the satisfactory results achieved by using artificial neural networks applications for forecasting. The aim of the thesis is to carry out a comparative analysis of the forecasting performance of different neural network models. The initial portion of the thesis will entail a study of the background as well as the different methodologies of artificial neural networks. Focus will be given to time series forecasting models. Results of forecasting different sets of data show that using neural network models gives almost the same, and in some cases better results, than other forecasting methods (Paplinski, 2003). Throughout this thesis paper, I intend to provide an introduction to neural networks, and the models applicable to time series forecasting. Particularly, in the feed-forward model of neural networks, I will provide some examples using R statistical programming language that will help the reader to observe a practical usage of R as a software that helps the user to compute complicated neural networking problems.