A comparative study of multilayer perceptron topologies for the selection of forecasting methods
Forecasting is an important and necessary activity for all types of organizations. Selecting the right forecasting method is a complex and time-consuming task. Nevertheless, the imperative to plan requires that organizations devote considerable resources to selecting appropriate forecasting methods to better anticipate future demand and determine resource allocation This research describes the use of a Neural network (specifically a multilayer perceptron) to select the most accurate forecasting method for any given time series. In addition, this paper examines the effect of increasing the number of hidden layers on the network's selection accuracy. Econometric time series data sets were used to train and test the neural networks. The results of this experiment, which appear promising, are presented together with guidelines for their practical application. Potential benefits include dramatic reductions in the effort and cost of forecasting, the provision of assistance to specialist forecasters and, of course, a substantial increase in forecasting accuracy.
forecasting neural networks artificial intelligence decision making