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A Multi-Layer Perceptron (MLP) defines a family of artificial neural networksoften used in TS modeling and forecasting. Because of its "black box" aspect,many researchers refuse to use it. Moreover, the optimization (often based onthe exhaustive approach where "all" configurations are tested) and learningphases of this artificial intelligence tool (often based on theLevenberg-Marquardt algorithm; LMA) are weaknesses of this approach(exhaustively and local minima). These two tasks must be repeated depending onthe knowledge of each new problem studied, making the process, long, laboriousand not systematically robust. In this paper a pruning process is proposed.This method allows, during the training phase, to carry out an inputs selectingmethod activating (or not) inter-nodes connections in order to verify ifforecasting is improved. We propose to use iteratively the popular dampedleast-squares method to activate inputs and neurons. A first pass is applied to10% of the learning sample to determine weights significantly different from 0and delete other. Then a classical batch process based on LMA is used with thenew MLP. The validation is done using 25 measured meteorological TS andcross-comparing the prediction results of the classical LMA and the 2-stageLMA.

Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method