### Abstract

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. © Springer-Verlag Berlin Heidelberg 2002.

Original language | English |
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Title of host publication | Robust estimator for the learning process in neural networks applied in time series |

Pages | 1080-1086 |

Number of pages | 7 |

ISBN (Electronic) | 9783540440741 |

Publication status | Published - 1 Jan 2002 |

Event | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duration: 1 Jan 2019 → … |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 2415 LNCS |

ISSN (Print) | 0302-9743 |

### Conference

Conference | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Period | 1/01/19 → … |

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## Cite this

Allende, H., Moraga, C., & Salas, R. (2002). Robust estimator for the learning process in neural networks applied in time series. In

*Robust estimator for the learning process in neural networks applied in time series*(pp. 1080-1086). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2415 LNCS).