A New Hybrid Method for Time Series Forecasting

This paper presents a new method — the Timedelay 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 is inspired in F. Takens theorem and 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. It is shown how this model proposed can boost the performance of time series prediction of both artificially generated time series and real world time series from the financial market. An experimental investigation is conducted with the TAEF method with five different relevant time series and the results achieved are discussed and compared with previous results found in the literature, according to several performance measures, showing the robustness of the proposed approach.