CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry

A highly accurate demand forecast contributes to higher profits with more sales opportunities and lower waste losses because of excess inventory in the modern retail industry. As forecasting models for chain stores require high generalization performance and operational efficiency, it is still challenging to build an efficient model in the demand side to timely cope with different purchasing characteristics among multiple regions using traditional learning schemes. Hence, this paper proposes a pooling attention and gated recurrent unit (PA-GRU) based learning model, with one convolution neural network (CNN) layer for feature extraction and one GRU for time series forecasting with two improved attention mechanisms, to enhance the demand forecast with higher accuracy. The first attention mechanism captures the trend and periodic patterns of the target variable, while the second attention mechanism refines the core features for further forecasting. Deep learning frameworks enable forecasting that adapts to time-varying patterns in different complex demands, which can also be efficiently applied for cross-learning in multi-stock keeping unit and multi-store environments, facilitated by an improved transfer learning scheme. Experiments are conducted based on a real-world POS dataset of a food supermarket with 3,065,572 customers in 46 stores in nine prefectures. The evaluation results demonstrate the outstanding performance of our proposed model in dealing with multiple time series data for demand forecasting in the retail industry compared to those of several existing learning methods.