Enhancing wheat price forecasting accuracy through Prophet based models

Accurate forecasting of agricultural commodity prices, especially wheat prices, is critical for stabilizing food markets and informing strategic decisions across supply chains. This study investigates optimal forecasting methodologies for wheat prices using Facebook's Prophet Tool. While Prophet's default configuration uses base forecasting patterns, its performance is significantly enhanced through hyperparameter optimization, including custom Fourier terms for crop-specific seasonality and adaptive changepoint detection to capture market shocks. To address residual volatility during geopolitical or climatic disruptions, the proposed framework integrates Prophet with gradient boosting algorithms for error correction, forming a hybrid model. The hybrid Prophet-ML approach further improves the accuracy of forecasting models. Empirical results, quantified through MAE, RMSE, and mean absolute percentage error (MAPE) metrics, underscore the framework's robustness in reconciling structural time series patterns with nonlinear market dynamics.