Forecasting grocery item sales using gradient boosting models: A study of GridSearchCV, RandomizedSearchCV, and optuna optimization approaches
Year: 2026
Type: article
Abstract: This study introduces predictive modeling based on Extreme Gradient Boosting (XGBoost), which utilizes Optuna for hyperparameter optimization and evaluates performance against GridSearchCV and RandomizedSearchCV using a 500-day dataset. To ensure statistical reliability of the findings, a bootstrapping with 1000 iterations is used to calculate 95% confidence intervals for all performance measures. Although GridSearchCV and RandomizedSearchCV achieve consistent performance, their average R-squared test performances are 0.81677 and 0.85538, respectively. In contrast, Optuna outperforms both methods by identifying better regions of optimal parameters, with an average R-squared of 0.94146. Furthermore, the computational efficiency analysis shows that Optuna’s average execution time of 28.31 s is a practical trade-off for running on consumer-grade hardware. Model interpretability is confirmed through Shapley Additive Explanations (SHAP) analysis, which identified the 3-day rolling average as the most important driving factor of demand.
Source: Ain Shams Engineering Journal
Authors: Puja Supakar, Mitali Sarkar, Biswajit Sarkar
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W7128953670