Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach

Abstract

Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is required. In particular, machine learning algorithms make it possible to build predictive models in order to forecast customer demand and, consequently, optimize the management of supplies and warehouse logistics. We base our analysis on the use of the XGBoost as a predictive model, since this is now considered to provide the more efficient implementation of gradient boosting, shown with a numerical comparison. Preliminary tests lead to the conclusion that the XGBoost regression model is more accurate in predicting future sales in terms of various error metrics, such as MSE (Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error). In particular, the improvement measured in tests using WAPE metric is in the range 15–20%.

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

“…The study also indicated that the incorporation of additional explanatory variables can minimize forecasting errors. Similarly, GBM and LightGBM were assessed for their utility in forecasting future sales and promotions, demonstrating decent accuracy [16][17][18]. XGBoost, a widely used model in demand forecasting due to its strong performance in sales forecasting for retail, was found to be a favorable choice [19].…”