Artificial neural networks in time series forecasting: a comparative analysis

Héctor Allende; Claudio Moraga; Rodrigo Salas

Kybernetika (2002)

  • Volume: 38, Issue: 6, page [685]-707
  • ISSN: 0023-5954

Abstract

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Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series models. We begin exploring recent developments in time series forecasting with particular emphasis on the use of non-linear models. Thereafter we include a review of recent results on the topic of ANN. The relevance of ANN models for the statistical methods is considered using time series prediction problems. Finally we construct asymptotic prediction intervals for ANN and show how to use prediction intervals to choose the number of nodes in the ANN.

How to cite

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Allende, Héctor, Moraga, Claudio, and Salas, Rodrigo. "Artificial neural networks in time series forecasting: a comparative analysis." Kybernetika 38.6 (2002): [685]-707. <http://eudml.org/doc/33612>.

@article{Allende2002,
abstract = {Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series models. We begin exploring recent developments in time series forecasting with particular emphasis on the use of non-linear models. Thereafter we include a review of recent results on the topic of ANN. The relevance of ANN models for the statistical methods is considered using time series prediction problems. Finally we construct asymptotic prediction intervals for ANN and show how to use prediction intervals to choose the number of nodes in the ANN.},
author = {Allende, Héctor, Moraga, Claudio, Salas, Rodrigo},
journal = {Kybernetika},
keywords = {artificial neural network; non-linear time series model; prediction; artificial neural network; non-linear time series model; prediction},
language = {eng},
number = {6},
pages = {[685]-707},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Artificial neural networks in time series forecasting: a comparative analysis},
url = {http://eudml.org/doc/33612},
volume = {38},
year = {2002},
}

TY - JOUR
AU - Allende, Héctor
AU - Moraga, Claudio
AU - Salas, Rodrigo
TI - Artificial neural networks in time series forecasting: a comparative analysis
JO - Kybernetika
PY - 2002
PB - Institute of Information Theory and Automation AS CR
VL - 38
IS - 6
SP - [685]
EP - 707
AB - Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series models. We begin exploring recent developments in time series forecasting with particular emphasis on the use of non-linear models. Thereafter we include a review of recent results on the topic of ANN. The relevance of ANN models for the statistical methods is considered using time series prediction problems. Finally we construct asymptotic prediction intervals for ANN and show how to use prediction intervals to choose the number of nodes in the ANN.
LA - eng
KW - artificial neural network; non-linear time series model; prediction; artificial neural network; non-linear time series model; prediction
UR - http://eudml.org/doc/33612
ER -

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