Abstract
This chapter presents a hybrid evolutionary computation/neural network combination for time series prediction. Neural networks are innate candidates for the forecasting domain due to advantages such as nonlinear learning and noise tolerance. However, the search for the ideal network structure is a complex and crucial task. Under this context, evolutionary computation, guided by the Bayesian Information Criterion, makes a promising global search approach for feature and model selection. A set of 10 time series, from different domains, were used to evaluate this strategy, comparing it with a heuristic model selection, as well as with conventional forecasting methods (e.g., Holt-Winters & Box-Jenkins methodology).
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