Meteorological time series forecasting based on MLP modelling using   heterogeneous transfer functions
  In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of measurements and the learning step, eight independent years. We show that this methodology can improve the accuracy of meteorological data estimation compared to a classical MLP modelling with a homogenous transfer function. 
Cyril Voyant (SPE), Marie Laure Nivet (SPE), Christophe Paoli (SPE),   Marc Muselli (SPE), Gilles Notton (SPE)
cs.LG