A new evolutionary method for time series forecasting

Authors:
Tiago A. E. Ferreira;Germano C. Vasconcelos;Paulo J. L. Adeodato
Affiliations:
Federal Univ. of Pernambuco, Recife - PE - Brazil;Federal Univ. of Pernambuco, Recife - PE - Brazil;Federal Univ. of Pernambuco, Recife - PE - Brazil
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Abstract

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 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.