Enhancing Enterprise Performance Through Forecasting: A Deep RNN Approach

Abstract

This research explores advanced methodologies in restaurant sales forecasting, focusing on a dataset from a Middle Eastern-based chain. It integrates machine learning algorithms and recurrent neural networks (RNN), including ARIMA, SARIMA, and LSTM models, to predict future sales trends accurately. Initial analysis includes various regression models to identify optimal sales prediction models, with Random Forest emerging as the top performer with an r2_score of 0.999895. The study emphasizes the importance of accurate sales forecasting in optimizing resource allocation, inventory management, and strategic decision-making for restaurant operations. Data preprocessing techniques such as missing value handling and feature selection ensure robust model performance. For time series forecasting, ARIMA and SARIMA models are applied to capture seasonal patterns, while LSTM models demonstrate ability in handling sequential data dependencies. The research employs comprehensive data integration strategies to unify diverse data sources into a cloud-based warehouse, enabling seamless analysis and forecasting. Results indicate that the selected models effectively predict restaurant sales, validating their applicability in real-world scenarios. The findings underscore the significance of machine learning and RNN models (LSTM) in enhancing sales prediction accuracy, thereby supporting informed business decisions in the competitive restaurant industry. Overall, this study contributes valuable insights into integrating advanced analytics for optimizing restaurant management and strategic planning.