Multi-Scale Forecasting of Natural Rubber Prices Using VMD-Augmented BiLSTM: A Hybrid Architecture Ablation Study

This study examines whether decomposition-based deep learning forecasts of daily changes in natural rubber prices can appear directionally accurate while failing to preserve the dispersion of the target series—a failure mode that conventional accuracy metrics cannot detect. Using daily RSS3 FOB price changes in the period 2018–2026, a VMD-Augmented BiLSTM forecasting design is employed as the empirical vehicle for testing this question. Forecasts are evaluated jointly through Pearson correlation, directional accuracy, class-conditional recall, and the Standard Deviation Ratio (StdR), with StdR serving as a diagnostic for variance collapse on differenced series. The deployed model appends all Variational Mode Decomposition (VMD) components directly to the economic feature matrix and feeds the augmented sequence into a bidirectional LSTM encoder with temporal attention; VMD is fitted using an expanding-window procedure to prevent information leakage. The design is compared to a conventional per-IMF decomposition–forecast pipeline, a Vanilla LSTM, ARIMA(2,0,2), and a dual-pathway BiLSTM–Transformer control. On a 175-observation deduplicated test set, the deployed model attains Pearson correlation of r = 0.821 ± 0.016 , directional accuracy of 82.5 % ± 1.8 % , and StdR = 1.091 ± 0.060 across five random seeds. The Vanilla LSTM baseline attains directional accuracy of 82.29 % ± 0.00 —statistically indistinguishable from that of the deployed model—yet exhibits variance collapse (StdR = 0.210 ± 0.007 ), confirming that DA alone cannot distinguish predictive skill grounded in conditional dynamics from forecasts that merely reproduce the unconditional sign distribution. The principal contribution is methodological: A variance-sensitive evaluation protocol that distinguishes forecast skill grounded in conditional dynamics from directional but underdispersed predictions, demonstrated across three empirically distinct mechanisms by which variance collapse arises in this setting.