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DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting

Created by
  • Haebom

Author

Haonan Yang, Jianchao Tang, Zhuo Li, Long Lan

Outline

This paper proposes a novel dynamic multi-scale scaling framework (DMSC) to address the challenge of modeling complex temporal dependencies across multiple scales in time series forecasting (TSF). DMSC consists of three core components: the multi-scale patch decomposition block (EMPD), the triple interaction block (TIB), and the adaptive scale-routing MoE block (ASR-MoE). EMPD dynamically partitions a sequence into hierarchical patches by adaptively adjusting the patch sizes of the input. The TIB comprehensively models intra-patch, inter-patch, and inter-variable dependencies within the decomposed representations of each layer. ASR-MoE dynamically fuses multi-scale predictions by leveraging specialized global and local experts with time-aware weighting. Experimental results on 13 real-world benchmarks demonstrate that DMSC achieves state-of-the-art performance and excellent computational efficiency. The code is available at https://github.com/1327679995/DMSC .

Takeaways, Limitations

Takeaways:
Solves the static decomposition strategy, fragmentary dependency modeling, and inflexible fusion mechanism of existing TSF methods.
Effectively model complex temporal dependencies through adaptive dynamic multi-scale decomposition and fusion to the input.
Achieves SOTA performance and superior computational efficiency in 13 real-world benchmarks.
Ensure reproducibility and extensibility through open code.
Limitations:
Further verification of the generalization performance of the proposed model is needed.
Potential performance bias for certain types of time series data.
Applicability and efficiency verification for more complex and large-scale time series data is required.
Lack of detailed description of model parameter tuning.
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