Time Series-Based Demand Forecasting: A Comparative Analysis of Holt-Winters, SARIMA, and Prophet Models on Retail Inventory Data

Accurate inventory management is a critical component of operational efficiency in the retail sector, directly influencing customer satisfaction and logistics cost optimization. Demand forecasting plays a pivotal role in this process by enabling businesses to anticipate future product needs and make data-driven decisions. This study aims to evaluate and compare the performance of classical time series models for demand forecasting using a rich and structured dataset. The analysis is based on the Retail Store Inventory Forecasting Dataset, a synthetic but realistic collection comprising over 73,000 daily sales records across multiple products and retail locations. Three prominent time series forecasting methods were selected for this research: the Holt-Winters Exponential Smoothing Model, the Seasonal ARIMA (SARIMA) model, and Prophet. These models were implemented to predict daily product demand and evaluated on a test set using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) as performance metrics. Among the three, the Holt-Winters model delivered the best forecasting performance with the lowest error values. The findings reveal that classical time series models remain powerful tools for retail forecasting tasks, especially when capturing patterns driven by seasonality and trend. Furthermore, this study demonstrates that robust forecasting techniques can substantially support inventory optimization efforts, helping to mitigate common challenges such as overstocking and stockouts. By highlighting the practical value of interpretable and well-established models, this research provides a foundational perspective for integrating time series forecasting into business intelligence and decision support systems.