Enhancing E-Commerce Demand Prediction Using Long Short-Term Memory Networks and Gradient Boosting Machines

Abstract

This research explores the integration of Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM) to enhance demand prediction in e-commerce environments, a critical factor for optimizing inventory management and logistics. We address challenges such as volatile consumer behavior, seasonality, and promotional impacts, which are prevalent in online retail. LSTM networks are leveraged to capture temporal dependencies within the data, providing a robust framework for modeling sequential patterns and long-range dependencies. The GBM is employed to enhance prediction accuracy by capturing non-linear relationships and interactions between variables. Our methodology involves a hybrid model where LSTM processes the temporal data component, while GBM refines these predictions based on feature importance and tree-based insights. The hybrid model is evaluated using a dataset from a leading e-commerce platform, comparing its performance against traditional models like ARIMA and standalone machine learning techniques. Results demonstrate a significant improvement in prediction accuracy, with a reduction in mean absolute error by 15% and mean squared error by 12% compared to the best traditional models. The integration of LSTM and GBM not only achieves superior predictive performance but also provides a scalable framework adaptable to varying data volumes and complexities inherent in e-commerce. This study contributes to the field by proposing a novel, effective approach to demand forecasting, highlighting the synergy between deep learning and ensemble methods in addressing the dynamic nature of e-commerce demand.