Robust estimator for the learning process in neural networks applied in time series

Héctor Allende, Claudio Moraga, Rodrigo Salas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

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 languageEnglish
Title of host publicationRobust estimator for the learning process in neural networks applied in time series
Pages1080-1086
Number of pages7
ISBN (Electronic)9783540440741
Publication statusPublished - 1 Jan 2002
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2415 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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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).