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Evolving Time Series Forecasting ARMA Models

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Abstract

Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such asOperational Research,Statistics orComputer Science. AlternativeTSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such asEvolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binaryEA will search for the bestARMA model, being the parameters optimized by a (low-level)EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.

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Author information

Authors and Affiliations

  1. Departamento de Sistemas de Informação, Campus de Azurém, Universidade do Minho, 4800-058, Guimarães, Portugal

    Paulo Cortez

  2. Departamento de Informática, Campus de Gualtar, Universidade do Minho, 4710-057, Braga, Portugal

    Miguel Rocha & José Neves

Authors
  1. Paulo Cortez

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  2. Miguel Rocha

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  3. José Neves

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