A new evolutionary method for time series forecasting

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. Categories and Subject Descriptors: I.6.5 [Model Development]: Modeling methodologies; I.2.8 [Problem Solving, Control Methods, and Search]: Heuristic methods General Terms: Experimentation.

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Tiago A. E. Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato
• Keywords: Characteristic Phase Space, GECCO 2005, Time Series, Time Series Prediction