Improving Demand Forecasting Through an Ensemble Method Using Adaptive Models and External Factors

To improve the accuracy of demand forecasting, this paper proposes a novel ensemble approach that incorporates deep learning techniques and traditional time series models, as well as external factors and adaptive mechanisms for market disruptions. Due to complex external influences and erratic market shifts, the retail industry still faces significant forecasting challenges. We tackle these issues by creating a comprehensive approach that makes use of the complementary advantages of four algorithms: Random Forest for managing promotional effects, LSTM networks for intricate temporal dependencies, Gradient Boosting for local pattern recognition, and SARIMAX for capturing seasonal patterns. Our adaptive weighting mechanism dynamically modifies model contributions according to identified market conditions and recent performance. We show that our ensemble approach improves forecast accuracy by 19 points compared to the best individual model using the Walmart retail dataset, which spans 45 stores across multiple departments. The Mean Absolute Percentage Error (MAPE) decreases from 9.3% to 7.5% improvement. Our approach is especially useful in volatile markets because it significantly shortens the recovery period after market disruptions by 44.7% (from 3 to 2 weeks). A major flaw in current forecasting systems is filled by the framework's capacity to adaptively adjust to changing conditions while methodically integrating external factors like temperature, promotions, economic indicators, and holidays. Our findings show that this integrated approach provides significant gains in accuracy and adaptability, with special advantages during times of promotion and market upheaval.