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ISNN
2010
Springer

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

10 years 11 months ago
MULP: A Multi-Layer Perceptron Application to Long-Term, Out-of-Sample Time Series Prediction
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.
Eros Pasero, Giovanni Raimondo, Suela Ruffa
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2010
Where ISNN
Authors Eros Pasero, Giovanni Raimondo, Suela Ruffa
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