a study for multi-objective fitness function for time series forecasting with intelligent techniques
This paper presents an study of a Hybrid method for time series prediction, called GRASPES, based on Greedy Randomized Adaptive Search Procedure (GRASP) Algorithm and Evolutionary Strategies (ES) concepts for tuning of the structure and parameters of an Artificial Neural Network (ANN). An experimental investigation with two time series is conducted and the results achieved are discussed and compared to other works reported in the literature. Distinct fitness functions evaluations are shown, instead of conventional MSE or NMSE based fitness functions evaluation. This results shown that small changes of the fitness function evaluation could lead to a significantly improved performance.
fitness function time series forecasting evolutionary strategies neural network