Robust Estimator for the Learning Process in Neural Networks Applied in Time Series

  • Héctor Allende
  • Claudio Moraga
  • Rodrigo Salas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)


Artificial Neural Networks (ANN) have been used to model non-linear time series as an alternative of the ARIMA models. In this paper Feedforward Neural Networks (FANN) are used as non-linear autoregressive (NAR) models. NAR models are shown to lack robustness to innovative and additive outliers. A single outlier can ruin an entire neural network fit. Neural networks are shown to model well in regions far from outliers, this is in contrast to linear models where the entire fit is ruined. We propose a robust algorithm for NAR models that is robust to innovative and additive outliers. This algorithm is based on the generalized maximum likelihood (GM) type estimators, which shows advantages over conventional least squares methods. This sensitivity to outliers is demostrated based on a synthetic data set.


Feedforward ANN Nonlinear Time Series Robust Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Héctor Allende
    • 1
    • 4
  • Claudio Moraga
    • 2
    • 3
  • Rodrigo Salas
    • 1
  1. 1.Dept. de InformáticaUniversidad Técnica Federico Santa MaríaCasillaChile
  2. 2.Dept. Artificial IntelligenceTechnical University of MadridBoadilla del Monte MadridSpain
  3. 3.Department of Computer ScienceUniversity of DortmundDortmundGermany
  4. 4.Facultad de Ciencia y TecnologíaUniversidad Adolfo IbañezGermany

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