A DEEP LEARNING APPROACH FOR PRODUCT DEMAND PREDICTION IN DYNAMIC MARKETS

This paper investigates the application of deep learning techniques for product demand prediction in dynamic markets, addressing the growing need for accurate forecasting in supply chain management. As consumer preferences become increasingly volatile and market conditions fluctuate, traditional statistical methods, such as Autoregressive Integrated Moving Averageand exponential smoothing, often fail to adapt effectively, resulting in significant forecasting inaccuracies. This study highlights the limitations of these conventional approaches and explores the potential of deep learning models, particularly Long Short-Term Memory networks, to enhance demand forecasting accuracy. By leveraging historical sales data and integrating various external factors, including economic indicators and market trends, deep learning models can capture complex nonlinear relationships and temporal dependencies that traditional methods overlook. The methodology comprises a comprehensive data collection and preprocessing phase, followed by the selection and training of the LSTM model. The results demonstrate that deep learning architectures significantly outperform traditional forecasting techniques, providing organizations with improved insights into demand patterns and enabling proactive decision-making. Furthermore, the paper emphasizes the necessity for models that can adapt to rapidly changing market conditions and suggests avenues for future research, including the exploration of hybrid models and the incorporation of additional data sources such as social media. The findings underscore the transformative potential of deep learning in demand forecasting, offering valuable implications for practitioners in supply chain management and contributing to the ongoing evolution of forecasting methodologies.