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

📰 ArXiv cs.AI

Learn how DMSC framework improves time series forecasting by dynamically coordinating multi-scale dependencies, and apply it to your TSF tasks

advanced Published 26 Jun 2026

Action Steps

Apply DMSC framework to your time series forecasting task to model intricate temporal dependencies
Use dynamic decomposition strategies to handle multi-scale dependencies
Implement novel architectures based on CNN, MLP or Transformer to improve forecasting accuracy
Configure fusion mechanisms to flexibly combine dependencies across different scales
Test DMSC framework on your dataset to evaluate its performance and compare with existing methods

Who Needs to Know This

Data scientists and researchers working on time series forecasting tasks can benefit from this framework to improve their model's performance and handle complex temporal dependencies

Key Insight

💡 DMSC framework addresses limitations of existing time series forecasting methods by dynamically coordinating multi-scale dependencies

Full Article

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

Abstract:
arXiv:2508.02753v5 Announce Type: replace-cross Abstract: 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

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