CONSTRUCTING A SALES FORECASTING MODEL BY INTEGRATING GRA AND ELM：A CASE STUDY FOR RETAIL INDUSTRY

Due to the strong competition and economic hardship, sales forecasting is a challenging problem as the demand fluctuation is influenced by many factors. A good forecasting model leads to improve the customers’ satisfaction, reduce destruction of fresh food, increase sales revenue and make production plan efficiently. In this study, the GELM forecasting model integrates Grey Relation Analysis (GRA) and extreme learning machine (ELM) to support purchasing decisions in the retail industry. GRA can sieve out the more influential factors from raw data and transforms them as the input data in a novel neural network such as ELM that can abandon the slow gradient-based learning speed and parameters tuned iteratively. The proposed system evaluated the real sales data of fresh food in the retail industry. The experimental results indicate the GELM model outperforms than other time series forecasting models, such as GARCH, GBPN and the GMFLN model in predicting accuracy and training speed. Otherwise, the different activation functions of the GELM model have significant differences in training time and performance during our experiments.