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A Hybrid Model for Symbolic Interval Time Series Forecasting

  • André Luis S. Maia
  • Francisco de A.T. de Carvalho
  • Teresa B. Ludermir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

This paper presents two approaches to symbolic interval time series forecasting. The first approach is based on the autoregressive moving average (ARMA) model and the second is based on a hybrid methodology that combines both ARMA and artificial neural network (ANN) models. In the proposed approaches, two models are respectively fitted to the mid-point and range of the interval values assumed by the symbolic interval time series in the learning set. The forecast of the lower and upper bounds of the interval value of the time series is accomplished through the combination of forecasts from the mid-point and range of the interval values. The evaluation of the proposed models is based on the estimation of the average behaviour of the mean absolute error and mean square error in the framework of a Monte Carlo experiment.

Keywords

Time Series Hybrid Model ARMA Model Time Series Forecast Monte Carlo Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • André Luis S. Maia
    • 1
  • Francisco de A.T. de Carvalho
    • 1
  • Teresa B. Ludermir
    • 1
  1. 1.Centro de Informatica – CIn/UFPERecifeBrazil

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