The goal of this presentation and associated paper is to present results of investigation related to use of the Extreme Gradient Boosting XGBoost algorithm as a forecasting tool. The data provided by the Rossman Com-pany, with a request to design an innovative prediction method, has been used as a base for this case study. The data contains details about micro- and macro-environment, as well as turnover of 1115 stores. Performance of the algorithm was compared to classical forecasting models SARIMAX and Holt-Winters, using time-series cross validation and tests for statistical importance in prediction quality dif-ferences. Metrics of root mean squared percentage error (RMSPE), Theil’s coeffi-cient and adjusted correlation coefficient were analyzed. Results where then passed to Rossman for verification on a separate validation set, via Kaggle.com platform. Study results confirmed, that XGBoost, after using proper data preparation and training method, achieves better results than classical models.