An Experimental Study of Fitness Function and Time Series Forecasting Using Artificial Neural Networks

Artificial Neural Networks (ANN) have been widely used in order to solve the time series forecasting problem and one of its most promising approach is the combination with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc. The choice of a good fitness function still an open question for the practitioners who use these techniques to solve the forecasting problem. The effectiveness and efficiency of the fitness functions proposed in the literature have not been compared among them. Based on five well-known (in the literature) measures of statistical errors and using three non linear time series, this paper empirically compares distinct fitness functions (instead of conventional MSE based ones). They are analysed using two hybrid methods for tuning ANN structure and parameters (a simplified but still realistic method called GRASPES and a modified genetic Algorithm).