The focus in the paper is on the information criteria approach and especially the Akaike information criterion which is used to obtain the Akaike weights. This approach enables to receive not one best model, but several plausible models for which the ranking can be built using the Akaike weights. This set of candidate models is the basis of calculating individual forecasts, and then for combining forecasts using the Akaike weights. The procedure of obtaining the combined forecasts using the AIC weights is proposed. The performance of combining forecasts with the AIC weights and equal weights with regard to individual forecasts obtained from models selected by the AIC criterion and the a posteriori selection method is compared in simulation experiment. The conditions when the Akaike weights are worth to use in combining forecasts were indicated. The use of the information criteria approach to obtain combined forecasts as an alternative to formal hypothesis testing was recommended.