6533b7d3fe1ef96bd1260823

RESEARCH PRODUCT

SIOPRED performance in a Forecasting Blind Competition

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In this paper we present the results obtained by applying our automatic forecasting support system, named SIOPRED, over a data set of time series in a Forecasting Blind Competition. In order to apply our procedure for providing point forecasts it has been necessary to develop an interactive strategy for the choice of the suitable length of the seasonal cycle and the seasonality form for a generalized exponential smoothing method, which have been obtained using SIOPRED. For the choice of those essential characteristics of forecasting methods, also a certain multi-objective formulation which minimizes several measures of fitting is used. Once these specifications are established, the model parameters (i.e. initial conditions and smoothing parameters) are also selected by using SIOPRED, which applies non-linear optimization and Soft Computing techniques without intervention by the forecaster in a completely automated way. Finally, our interactive proposal uses a multi-objective approach for selecting the data pattern.