Abstract

Accurately forecasting metal prices is critical to economic, industrial, and energy markets. However, traditional time series models often rely on future data, limiting their real-world applicability. This study found that decomposition methods utilizing future data inflate model performance, and the high accuracy of forecasting models is mainly due to these unrealistic assumptions. In this paper, we propose a novel Temporal Consistency Ensemble Empirical Mode Decomposition (TC-EEMD) method designed for rolling scenarios for practical forecasting models. The performance of the method was evaluated using Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer on real precious metal price data. The results show that TC-EEMD and hybrid approaches improve forecast accuracy and robustness, mitigating noise dependence and stabilizing forecasts across different industrial applications.