Ensemble-Based Stock Prediction for Retail - XGBoost and LightGBM with Rolling Window Training

Abstract

Stock prediction in retail settings is a critical challenge that impacts numerous businesses globally, that require precise and timely forecasts to optimize inventory management and enhance customer satisfaction. State-of-the-art approaches for accurate stock prediction leverage machine learning (ML) models, which require large amounts of historical sales data for effective training. Such detailed datasets are often hard to obtain, limiting the performance and scalability of these approaches. In this paper, we propose various strategies to tackle this limitation. Initially, we adopt a transfer-learning approach, utilizing pre-trained models like XGBoost and LightGBM, which are fine-tuned for stock prediction in retail environments. To further boost model performance, we incorporate an ensemble method that combines predictions from both models to improve accuracy and manage outliers. Experiments conducted on an extremely large dataset, comprising millions of retail transactions, highlight the presence of significant outliers. Our models, augmented with ensemble strategies, significantly outperform traditional models in handling these complexities and improving prediction accuracy. © 2025 Elsevier B.V., All rights reserved.Stock prediction in retail settings is a critical challenge that impacts numerous businesses globally, that require precise and timely forecasts to optimize inventory management and enhance customer satisfaction. State-of-the-art approaches for accurate stock prediction leverage machine learning (ML) models, which require large amounts of historical sales data for effective training. Such detailed datasets are often hard to obtain, limiting the performance and scalability of these approaches. In this paper, we propose various strategies to tackle this limitation. Initially, we adopt a transfer-learning approach, utilizing pre-trained models like XGBoost and LightGBM, which are fine-tuned for stock prediction in retail environments. To further boost model performance, we incorporate an ensemble method that combines predictions from both models to improve accuracy and manage outliers. Experiments conducted on an extremely large dataset, comprising millions of retail transactions, highlight the presence of significant outliers. Our models, augmented with ensemble strategies, significantly outperform traditional models in handling these complexities and improving prediction accuracy.