Deep Learning Techniques for Demand Forecasting: Review and Future Research Opportunities

The aim of this study is to categorize research on the applications of deep learning techniques in demand forecasting and suggest further research directions. This study is based upon 56 papers published between 2017 and April 2021 in international peer-reviewed elite journals. The primary objective of this paper is to identify the major problem domains in demand forecasting; hence, the authors conduct a review of literature that utilizes deep learning techniques for demand forecasting and proposed directions for future research. After identifying the objective, a subject scrutiny of the important papers is done based on the publication quality. These identifications make additions to demand forecasting research in the resulting manner. For accomplishing this task, first, the authors classified the literature into nine major problem domains based on different issues discussed in the literature. Second, the literature is classified based on different deep leaning techniques used for solving the problem of demand forecasting. Third, seven research propositions are provided for future research.