Abstract
Due to the strong competition that exists today, most retailers are in a continuous effort for increasing profits and reducing their cost. An accurate sales forecasting system is an efficient way to achieve the aforementioned goals and lead to improve the customers’ satisfaction, reduce destruction of products, increase sales revenue and make production plan efficiently. While manage the convenience store, the supervisor should estimate the daily demand of the future and place an order to purchase the commodities. If the managers can estimate the probable sales quantity in the next period, the demand could be satisfied and the cost of spoiled fresh foods would substantially be reduced. Besides a good forecasting model leads to improve the customers’ satisfaction, reduce destruction of fresh food, increase sales revenue and make production plan efficiently. This study constructs a retailing sales forecasting model by data mining framework. Firstly, it applies GRA to realize the relationship between two sets of time series data in relational space then sieves out the more influential factors from raw data and transforms them as the input data for developing the forecasting model. Secondly, this research applies time series forecasting model includes MA, ARIMA, GARCH and neural network forecasting model includes BPN, MFLN and ELM. The proposed system evaluated the real sales data in the retail industry. The experimental results demonstrate that our proposed system which integrates GRA and ELM based on robust experiments design with Taguchi method outperforms than other sales forecasting methods based on time series and neural networks methodology.