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.
This work was supported in part by Research Grant Fondecyt 1010101, in part by Research Grant BMBF CHL-99/023 (Germany) and in part by Research Grant DGIP-UTFSM. Work of C. Moraga was supported by Grant SAB2000-0048 of the Ministry of Education, Culture and Sport (Spain) and the Social Fund of the European Union.
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Allende, H., Moraga, C., Salas, R. (2002). Robust Estimator for the Learning Process in Neural Networks Applied in Time Series. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_175
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DOI: https://doi.org/10.1007/3-540-46084-5_175
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