Data transformations and seasonality adjustments improve forecasts of MLP ensembles

This work describes the first place winner forecasting method for solving the 1st International Competition on Time Series Forecasting (ICTSF 2012). It is based on an already award winning approach of MLP ensembles [1]. The ICTSF 2012 consisted on predicting 8 time series of different time frequency and different forecasting horizons. The main feature of the present method was applying different data pre-processing and seasonality adjustments to a combined forecast of 225 MLPs predicting each time series. Experimental comparison and the competitions result shows that this new predictive system increases its performance in multi-step forecasting when compared to ensembles of MLP.