Forecasting Strategies in Retail: Utilizing Advanced Machine Learning Methods while Safeguarding Privacy

Abstract Demand forecasting is critical to optimizing retail inventory management, pricing, and operations. This study compares several time series forecasting models on a publicly available retail dataset, including traditional stats like ARIMA and SARIMA, smoothing techniques, and new ones from Facebook, such as Prophet and Neural Prophet. We evaluate each model using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R 2 . The results show that NeuralProphet and hybrid models outperform traditional models like ARIMA and SARIMA. We also look at the computational efficiency and practicality of the models and provide insights into how they can be used in real-world demand forecasting scenarios. The results show that traditional models are robust. Still, advanced models like NeuralProphet and hybrids, which combine machine learning with time series forecasting, have a lot of potential to improve forecast accuracy and operational decision-making.