A hybrid method for tuning neural network for time series forecasting

Authors:
Aranildo Rodrigues Lima Junior;Tiago Alessandro Espínola Ferreira
Affiliations:
Federal University of Pernambuco, Recife, Brazil;Rural Federal University of Pernambuco, Recife, Brazil
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 3
Cited 3

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Abstract

This paper presents an study about a new Hybrid method -GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatorial problems - Greedy Randomized Adaptive Search Procedure(GRASP) - and Evolutionary Strategies (ES) concepts. The GRAPES tuning and evolve the Artificial Neural Network parameters configuration, the weights and the minimum number of (and their specific) relevant time lags, searching an optimal or sub-optimal forecasting model for a correct time series representation. An experimental investigation is conducted with the GRASPES with some time series and the results achieved are discussed and compared, according to five well-known performance measures, to other works reported in the literature.