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