TIME SERIES FORECASTING IN BUSINESS INTELLIGENCE: A COMPARATIVE STUDY OF CLASSICAL AND MACHINE LEARNING APPROACHES FOR SALES TREND PREDICTION
Ramchorn Gharami, Delwar Karim, Amit Kumar, Rashid Khan · Journal of trends in financial and economics. · 2025
Sales trend forecasting is a critical function within modern business intelligence (BI) systems, enabling organizations to optimize inventory management, allocate resources effectively, and make strategic decisions in an increasingly volatile market. Traditionally, classical time series models such as ARIMA and Exponential Smoothing have been widely used due to their interpretability and robust theoretical foundations. However, the emergence of machine learning (ML) methods such as Random Forests, Gradient Boosting Machines, and Long Short-Term Memory (LSTM) networks has introduced new opportunities for capturing complex, non-linear patterns in sales data. This review provides a comprehensive comparison between classical and machine learning approaches for sales trend prediction, examining their strengths, limitations, and practical applications. Classical models offer simplicity, computational efficiency, and strong performance with stationary and linear data, making them suitable for well-structured datasets. Conversely, machine learning models excel in handling large, noisy, and multi-dimensional datasets, offering superior accuracy at the cost of higher computational demands and reduced interpretability. Real-world applications across retail, finance, supply chain management, and healthcare are explored, highlighting the transformative impact of time series forecasting in business operations. Key challenges including data quality, model maintenance, and the need for explainable AI are discussed alongside future directions such as real-time forecasting, transfer learning, and federated learning. By synthesizing insights from both classical and contemporary forecasting paradigms, this review aims to guide researchers, data scientists, and business leaders in selecting appropriate methodologies for enhancing predictive capabilities within business intelligence ecosystems.