Sales Forecasting Using ANNs or RNNs - A Case Study of Freeway Service Station in Taiwan

Following recent progress in neural networks, an increasing number of researchers have applied this technique to sales forecasting. The accurate prediction of sales enables businesses to reduce stockpiles and scrap costs. However, it has been heavily debated whether artificial neural networks (ANNs) or recurrent neural networks (RNNs) are the most appropriate for sales forecasting. A number of factors influence sales, and it is difficult to determine whether daily business conditions are independent of each other. To fill this gap in the literature, we first employed conventional data analysis to identify suitable input fields for ANN and RNN. We then input these fields into both types of neural network. To verify the validity of our discussion, we conducted an analysis using a real-world sales dataset from a service station on a freeway in Taiwan.