Leveraging LSTM Neural Networks and ARIMA Models for Enhanced Real-Time Sales Forecasting in Dynamic Retail Environments

Abstract

This paper investigates the synergistic integration of Long Short-Term Memory (LSTM) neural networks and AutoRegressive Integrated Moving Average (ARIMA) models to enhance real-time sales forecasting in dynamic retail environments. The volatility and complexity inherent in retail demand patterns necessitate advanced predictive methodologies to optimize inventory management and improve decision-making processes. We propose a hybrid model that combines the deep learning capabilities of LSTM, which excels in capturing non-linear patterns and temporal dependencies, with the robust statistical foundation of ARIMA, known for its proficiency in modeling linear time series components and seasonality. By utilizing a large dataset drawn from multiple retail outlets spanning diverse geographical locations and product categories, our study conducts comprehensive experiments to evaluate the individual and combined performances of these models. The results demonstrate that the hybrid approach significantly outperforms standalone LSTM and ARIMA models in terms of prediction accuracy and adaptability to abrupt market shifts. Additionally, the proposed method exhibits superior computational efficiency, making it suitable for real-time deployment. This research contributes to the growing field of machine learning-driven forecasting by offering a practical solution that empowers retailers to anticipate demand fluctuations with greater precision, ultimately leading to improved customer satisfaction and operational efficiency.