Explainable Ensemble Learning for Sales Forecasting and Product Ranking in Retail

The current study explores a framework for retail
sales prediction and product selection based on approximately
50,000 sales transactions. Time-based variables such as lagged
sales values, rolling averages, and cyclic month encoding have
been designed to detect sequential purchases. XGBoost, LightGBM, CatBoost, and a weighted ensemble of the previous algorithms have been tested by means of 5-fold time-sensitive crossvalidation with RMSE as a metric. The lowest RMSE of 3958.76
was achieved by CatBoost, outperforming other models such as
XGBoost (4951.30), the weighted hybrid ensemble (5454.23), and
LightGBM (7986.99). Analysis of SHAP values demonstrated
that the most relevant variable is lag 1. Iterative multi-step
forecasting with horizons of 6 and 12 months showed that ”TShirt” and ”Headphones” are top products, while electronics
ranked among the best. These findings indicate that appropriate
engineering of time-based features, gradient boosting algorithms,
and explainable AI techniques can reliably help to make decisions
on inventory management and marketing, but the current model
is limited due to the lack of external variables and accumulation
of errors during iterative forecast