Enriching demand prediction with product relationship information using graph neural networks

Demand prediction is crucial for companies in the retail industry to increase their profit and customer satisfaction. Although recent studies show the success of state-of-art machine learning and deep learning models in demand prediction, enriching datasets using graph-based feature representations to improve demand forecasting models is still rare. In this study, we propose a demand forecasting model that forecasts demand with the usage of graph-based product embeddings. Unlike most of the existing methods, the sale information data is used to extract the relations and several relationships are utilized to construct graphs. Using the Node2Vec and GraphSAGE algorithms, five different embeddings are evaluated to reflect the different relationships of products. Extreme Gradient Boosting Regressor (XGBR) is preferred over other models because of the ability to handle high sparse data. In order to observe and compare the results of different models, we also implement Long Short Term Memory (LSTM). The performance is evaluated using a public retail dataset and the results show that the proposed model gives less error using Node2Vec graph-based embedding with XGBR.