Multi-Scale Forecasting of Natural Rubber Prices Using a VMD-Hybrid BiLSTM-Transformer Framework

Natural rubber price forecasting is inherently difficult due to nonlinear, non-stationary dynamics driven by supply fundamentals, cross-market signals, exchange rate movements, and speculative trading. This study proposes VMD–Hybrid BiLSTM–Transformer, a dual-pathway framework integrating Variational Mode Decomposition (VMD) with a Bidirectional LSTM encoder and a Transformer encoder for daily RSS3 FOB price change forecasting. Rather than forecasting each intrinsic mode function independently, all five VMD components are appended directly to the economic feature matrix — preserving multi-scale frequency information within a single forward pass and avoiding the variance collapse observed in conventional decomposition forecast approaches (StdR = 0.04 - 0.15). On a 237-observation held-out test set (September 2025–February 2026), the model achieves Pearson correlation of 0.812, directional accuracy of 67.1%, and StdR of 0.819, outperforming ARIMA by 0.662 in correlation and 37.3% in MAE, with predictive skill confirmed up to five days. These results demonstrate that directional accuracy alone is insufficient for evaluating difference commodity price models, and that jointly integrating multi-scale decomposition, bidirectional learning, and global attention is essential for reliable agricultural price forecasting.