An Extreme Gradient Boosting Model Optimized with Genetic Algorithm for Sales Forecasting of Retail Stores

Aziz Kemal Konyalıoğlu*, Tuğçe Beldek Apaydın, İlhan Turhan, Adil Soydal, Tuncay Özcan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Forecasting has always been a curious topic to investigate for practitioners, academics and workers in private companies. Not only in the world but also In Turkey, COVID-19 pandemic makes difficult to forecast sales for any type of companies since patterns, sales and seasonality factors in sales have changed because of different reasons. At this point, the accuracy of sales forecasts is of great importance for retail companies. In particular, sales forecasts affect the decisions and actions taken on a daily and weekly basis. In this study, firstly, a model based on the Extreme Gradient Boosting (XGBoost) algorithm is proposed for daily sales forecasting of retail stores. Later, a hybrid GA-XGBoost model is developed to improve the performance of this model. In this model, the parameters of XGBoost are optimized by Genetic Algorithm. Finally, the performance of the developed model is compared with the SARIMA model using the root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared. The performance comparison is demonstrated by a case study with data from airport stores of a retail chain in Turkey. Numerical results show that the hybrid XGBoost-GA model outperforms the XGBoost and SARIMA models.

Original languageEnglish
Title of host publicationIndustrial Engineering in the Industry 4.0 Era - Selected Papers from ISPR2023
EditorsNuman M. Durakbasa, M. Güneş Gençyılmaz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783031539909
Publication statusPublished - 2024
EventInternational Symposium for Production Research, ISPR 2023 - Antalya, Turkey
Duration: 5 Oct 20237 Oct 2023

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364


ConferenceInternational Symposium for Production Research, ISPR 2023

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.


  • Extreme Gradient Boosting
  • Genetic Algorithm
  • Retailing
  • Sales Forecasting


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