Enhancing Predictive Business Analytics with Deep Learning and Ensemble Methods: A Comparative Study of LSTM Networks and Random Forest Algorithms

Abstract

This study investigates the enhancement of predictive business analytics through the integration of deep learning and ensemble methods, specifically focusing on Long Short-Term Memory (LSTM) networks and Random Forest algorithms. The research addresses the increasing demand for accurate forecasting models that can anticipate complex business trends and patterns. We conducted a comparative analysis to evaluate the performance and applicability of LSTM networks, known for their ability to handle sequential data and capture temporal dependencies, against Random Forest algorithms, which are renowned for their robustness in handling non-linear data and reducing overfitting. Our methodology involved the deployment of both models on extensive datasets encompassing various business sectors, including finance, retail, and supply chain management, aiming to predict key performance indicators such as sales, stock prices, and demand levels. The results demonstrated that LSTM networks outperform Random Forests in scenarios requiring the analysis of time-dependent data, providing superior accuracy in forecasting long-term trends. Conversely, Random Forests exhibited better performance in datasets characterized by high dimensionality and complex feature interactions, where they offered enhanced interpretability and faster computation times. Furthermore, we explored the potential of hybrid models combining the strengths of both approaches, leading to improved predictive capabilities. This paper contributes to the field by offering valuable insights into selecting and implementing advanced predictive models in business analytics, ultimately facilitating informed decision-making and strategic planning.