A forecasting approach based on Multi-Layer Perceptron (MLP) Artificial Neural Networks (named by the authors MULP) is proposed for the NN5 111 time series long-term, out of sample forecasting competition. This approach follows a direct prediction strategy and is completely automatic. It has been chosen after having been compared with other regression methods (as for example Support Vector Machines (SVMs)) and with a recursive approach to prediction. Good results have also been obtained using the ANNs forecaster together with a dimensional reduction of the input features space performed through a Principal Component Analysis (PCA) and a proper information theory based backward selection algorithm. Using this methodology we took the 10th place among the best 50% scorers in the final results table of the NN5 competition.

MULP: A Multi-Layer Perceptron Application to Long-Term, Out-of-Sample Time Series Prediction / Pasero, Eros Gian Alessandro; Raimondo, Giovanni; Ruffa, Suela. - STAMPA. - 6064:(2010), pp. 566-575. ((Intervento presentato al convegno 7th International Symposium on Neural Networks, ISNN 2010 tenutosi a Shanghai nel 6-9 June 2010 [10.1007/978-3-642-13318-3_70].

MULP: A Multi-Layer Perceptron Application to Long-Term, Out-of-Sample Time Series Prediction

PASERO, Eros Gian Alessandro;RAIMONDO, Giovanni;RUFFA, SUELA
2010

Abstract

A forecasting approach based on Multi-Layer Perceptron (MLP) Artificial Neural Networks (named by the authors MULP) is proposed for the NN5 111 time series long-term, out of sample forecasting competition. This approach follows a direct prediction strategy and is completely automatic. It has been chosen after having been compared with other regression methods (as for example Support Vector Machines (SVMs)) and with a recursive approach to prediction. Good results have also been obtained using the ANNs forecaster together with a dimensional reduction of the input features space performed through a Principal Component Analysis (PCA) and a proper information theory based backward selection algorithm. Using this methodology we took the 10th place among the best 50% scorers in the final results table of the NN5 competition.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2370239
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