Article
DEMAND FORECASTING USING RANDOM FOREST, GRADIENT BOOSTING, AND LSTM ALGORITHMS
Demand forecasting is a fundamental component in supply chain management, enabling organizations to predict future product demand and optimize inventory, production, and logistics. Traditional statistical approaches often struggle with nonlinear relationships, seasonality, and high-dimensional data. This paper proposes an advanced forecasting framework integrating machine learning and deep learning models—Random Forest (RF), Gradient Boosting (GB), and Long Short-Term Memory (LSTM). The study emphasizes handling temporal dependencies, feature interactions, and uncertainty in demand patterns. A comparative analysis is conducted using real-world-like datasets, evaluating models based on MAE, RMSE, and MAPE. Experimental findings indicate that LSTM captures temporal dependencies effectively, while ensemble methods ensure robustness and interpretability. The hybrid modeling approach significantly improves forecasting accuracy and scalability in dynamic environments.
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