arXiv:2508.02753v5 Announce Type: replace 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 dependencies. To explicitly solve the mentioned three problems respectively, we propose a novel Dynamic Mu
This research addresses the persistent challenges in time series forecasting, a critical component for AI applications, coming at a time of increasing demand for accurate predictive models across industries.
Improved time series forecasting allows for more accurate predictions in complex systems, enhancing decision-making in finance, logistics, resource management, and potentially autonomous AI systems.
The proposed DMSC framework offers a new approach to dynamic multi-scale coordination in time series forecasting, potentially leading to more robust and adaptable predictive models compared to existing static methods.
- · AI developers
- · Data analytics companies
- · Finance industry
- · Logistics and supply chain management
- · Companies relying on static or less sophisticated forecasting models
- · Industries prone to high prediction error
More accurate predictive models become accessible, improving operational efficiency and reducing risk in various sectors.
Enhanced forecasting capabilities could lead to new financial products, optimized infrastructure management, and more resilient supply chains.
The increased reliability of AI predictions might accelerate the deployment of autonomous decision-making systems in traditionally human-managed domains.
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Read at arXiv cs.LG