Enhancing Predictive Sales Analytics Using LSTM Networks and Random Forest Algorithms

Abstract

This research paper explores the enhancement of predictive sales analytics by integrating Long Short-Term Memory (LSTM) networks and Random Forest algorithms. The study addresses the challenges posed by dynamic market conditions and consumer behavior shifts, aiming to improve the accuracy and reliability of sales forecasts. We first outline limitations of traditional linear models in capturing nonlinear patterns and temporal dependencies in sales data. Subsequently, we propose a hybrid model that leverages the sequential learning capabilities of LSTM networks alongside the robust decision-tree framework of Random Forests. The methodology involves training the LSTM network to model long-term dependencies and temporal patterns, while the Random Forest algorithm captures nonlinear relationships and reduces model variance. We validate our approach using a comprehensive dataset from a multinational retail enterprise, comparing its performance with standalone LSTM and Random Forest models as well as conventional statistical methods. The hybrid model demonstrates significant improvement in predictive accuracy, measuring a 15-20% decrease in mean absolute error compared to individual models. Furthermore, the model shows robustness in handling missing data and adaptability across different product categories. These findings suggest that the integration of LSTM networks and Random Forest algorithms provides a powerful tool for sales forecasting, offering enhanced insights for business decision-making and strategic planning. Future work will explore the scalability of this model in real-time analytics and its application across various industries.