Causal Method and Time Series Forecasting model based on Artificial Neural Network

Abstract

This article discusses two methods of dealing with demand variability. First a causal method based on multiple regression and artificial neural networks have been used. The ANN is trained for different structures and the best is retained. Secondly a multilayer perceptron model for time series forecasting is proposed. Several learning rules used to adjust the ANN weights have been evaluated. The results show that the performances obtained by the two methods are very similar. The cost criterion is then used to choose the appropriate model.