Forecasting of retail sales data using differential evolution

The paper aims to develop an efficient forecasting model using differential evolution (DE) based learning rule. The structure chosen is an adaptive linear combiner whose weights are trained using DE. The prediction performance of the resulting model is evaluated by feeding features of retail sales data for different months´ ahead prediction. These results are compared with those obtained by GA based approach. The comparison demonstrates improved prediction of sales data by the proposed DE method.