Kavli Affiliate: Zhuo Li
| First 5 Authors: Haonan Yang, Haonan Yang, , ,
| Summary:
Time Series Forecasting (TSF) faces persistent challenges in modeling
intricate temporal dependencies across different scales. Despite recent
advances leveraging different decomposition operations and novel architectures
based on CNN, MLP or Transformer, existing methods still struggle with static
decomposition strategies, fragmented dependency modeling, and inflexible fusion
mechanisms, limiting their ability to model intricate temporal dependencies. To
explicitly solve the mentioned three problems respectively, we propose a novel
Dynamic Multi-Scale Coordination Framework (DMSC) with Multi-Scale Patch
Decomposition block (EMPD), Triad Interaction Block (TIB) and Adaptive Scale
Routing MoE block (ASR-MoE). Specifically, EMPD is designed as a built-in
component to dynamically segment sequences into hierarchical patches with
exponentially scaled granularities, eliminating predefined scale constraints
through input-adaptive patch adjustment. TIB then jointly models intra-patch,
inter-patch, and cross-variable dependencies within each layer’s decomposed
representations. EMPD and TIB are jointly integrated into layers forming a
multi-layer progressive cascade architecture, where coarse-grained
representations from earlier layers adaptively guide fine-grained feature
extraction in subsequent layers via gated pathways. And ASR-MoE dynamically
fuses multi-scale predictions by leveraging specialized global and local
experts with temporal-aware weighting. Comprehensive experiments on thirteen
real-world benchmarks demonstrate that DMSC consistently maintains
state-of-the-art (SOTA) performance and superior computational efficiency for
TSF tasks. Code is available at https://github.com/1327679995/DMSC.
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