PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
This work addresses the problem of volatile and nonlinear commodity demand forecasting for economists and market analysts, offering an interpretable and scalable solution, though it is incremental as it builds on existing GRU and PINN methods.
The paper tackled commodity demand forecasting by introducing PREIG, a framework that integrates GRU with physics-informed constraints and hybrid optimization, achieving significant improvements in RMSE and MAPE over traditional econometric and deep learning baselines.
Accurately forecasting commodity demand remains a critical challenge due to volatile market dynamics, nonlinear dependencies, and the need for economically consistent predictions. This paper introduces PREIG, a novel deep learning framework tailored for commodity demand forecasting. The model uniquely integrates a Gated Recurrent Unit (GRU) architecture with physics-informed neural network (PINN) principles by embedding a domain-specific economic constraint: the negative elasticity between price and demand. This constraint is enforced through a customized loss function that penalizes violations of the physical rule, ensuring that model predictions remain interpretable and aligned with economic theory. To further enhance predictive performance and stability, PREIG incorporates a hybrid optimization strategy that couples NAdam and L-BFGS with Population-Based Training (POP). Experiments across multiple commodities datasets demonstrate that PREIG significantly outperforms traditional econometric models (ARIMA,GARCH) and deep learning baselines (BPNN,RNN) in both RMSE and MAPE. When compared with GRU,PREIG maintains good explainability while still performing well in prediction. By bridging domain knowledge, optimization theory and deep learning, PREIG provides a robust, interpretable, and scalable solution for high-dimensional nonlinear time series forecasting in economy.