Title: Towards Accurate Retail Demand Forecasting Using Deep Neural Networks
Authors: Shanhe Liao, Jiaming Yin and Weixiong Rao
Abstract: Accurate product sales forecasting, or known as demand forecasting, is important for retails to avoid either insu?cient or excess inventory in product warehouse. Traditional works adopt either univariate time series models or multivariate time series models. Unfortunately, previous prediction methods frequently ignore the inherent structural information of product items such as the relations between product items and brands and the relations among various product items, and cannot perform accurate forecast. To this end, in this paper, we propose a deep learning-based prediction model, namely Structural Temporal Attention network (STANet), to adaptively capture the inherent interdependencies and temporal characteristics among product items. STANet uses the graph attention network and a variable-wise temporal attention to extract inter-dependencies among product items and to discover dynamic temporal characteristics, respectively. Evaluation on two realworld datasets validates that our model can achieve better results when compared with state-of-the-art methods.
