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A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks

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Abstract

The Time-delay Added Evolutionary Forecasting (TAEF) approach is a new method for time series prediction that performs an evolutionary search for the minimum number of dimensions necessary to represent the underlying information that generates the time series. The methodology proposed is inspired in Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network combined with a modified genetic algorithm. Initially, the TAEF method finds the best fitted model to forecast 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. An experimental investigation conducted with relevant time series show the robustness of the method through a comparison, according to several performance measures, to previous results found in the literature and those obtained with more traditional methods.

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Authors and Affiliations

  1. Statistics and Informatics Department, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, s/n, Dois Irmãos, 52171-900, Recife, Pernambuco, Brazil

    Tiago A. E. Ferreira

  2. Center for Informatics, Federal University of Pernambuco, Av. Prof. Luiz Freire, s/n, Cidade Universitria, 50732-970, Recife, Pernambuco, Brazil

    Germano C. Vasconcelos & Paulo J. L. Adeodato

Authors
  1. Tiago A. E. Ferreira
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  2. Germano C. Vasconcelos
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  3. Paulo J. L. Adeodato
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Correspondence to Tiago A. E. Ferreira.

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Ferreira, T.A.E., Vasconcelos, G.C. & Adeodato, P.J.L. A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks. Neural Process Lett 28, 113–129 (2008). https://doi.org/10.1007/s11063-008-9085-x

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  • DOI: https://doi.org/10.1007/s11063-008-9085-x

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