Beyond Point Predictions: Manifold Expansion and Dual Alignment for Robust Time Series Distillation

Knowledge Distillation (KD) promises to bridge the gap between the high computational costs of Transformer-based models and the expressiveness limitations of linear models in long-term time series forecasting. Many existing time series distillation methods inherit the computer vision paradigm, constraining student models by minimizing point-wise prediction matching (output-level distillation) errors. However, blindly mimicking teacher predictions, which can be uncertain, may induce negative transfer. To address this, we propose Dynamic Structural Distillation (DSD), a robust framework that goes beyond the prediction-matching paradigm. First, we design LMP-Net, leveraging manifold expansion to project features into a high-dimensional latent space, alleviating the expressiveness bottleneck while preserving lightweight inference. Second, to address token–point architectural mismatch, we propose Dual Manifold Alignment, employing Similarity-Preserving Knowledge Distillation (SPKD) and Optimal Transport (OT) to align features at the topological and geometric levels, respectively. Finally, we introduce Regime-Aware Adaptive Distillation (RAAD) to reduce the risk of teacher misguidance via a dataset-level regime prior and a confidence-based adaptive gating mechanism. Extensive experiments on five benchmarks show that DSD is compatible with diverse teacher architectures, improves lightweight students across data regimes, and achieves a favorable accuracy–efficiency trade-off. Code is available at https://github.com/jyh0526/DSD.