3 papers
cs.LG2025
From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs
Hongwei Ma, Junbin Gao, Minh-ngoc Tran
Long-horizon multivariate time-series forecasting is challenging because realistic predictions must (i) denoise heterogeneous signals, (ii) track time-varying cross-series dependen…
cs.LG2025
Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting
Hongwei Ma, Junbin Gao, Minh-Ngoc Tran
Accurate, explainable and physically credible forecasting remains a persistent challenge for multivariate time-series whose statistical properties vary across domains. We propose D…
cs.LG2025
PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
Hongwei Ma, Junbin Gao, Minh-Ngoc Tran
Accurately forecasting commodity demand remains a critical challenge due to volatile market dynamics, nonlinear dependencies, and the need for economically consistent predictions.…