A New Hybrid Approach for Enhanced Times Series Prediction

Tiago A. E. FerreiraGermano C. VasconcelosPaulo J. L. Adeodato

This paper presents a new method — the Time-delay Added Evolutionary Forecasting (TAEF) method—for time series prediction which performs an evolutionary search of the minimum necessary number of dimensions embedded in the problem for determining the characteristic phase space of the phenomenon generating the time series. The method proposed consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with a modified genetic algorithm (GA). Initially, the TAEF method finds the most fitted predictor model for representing the series and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of some series.

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