arXiv:2605.25166v1 Announce Type: new Abstract: Time series forecasting models are increasingly scaled through large Transformer backbones, yet most existing approaches process all series through a shared dense computation path despite substantial heterogeneity in temporal structure. Mixture-of-Experts (MoE) offers a natural alternative by enabling conditional computation, but standard MoE routing leaves expert specialization weakly identified and often unstable during downstream adaptation. We propose AME-TS, a structure-guided sparse time series foundation model that aligns expert routing wi
This research is emerging as large AI models, particularly Transformers, are being scaled for increasingly complex tasks like time series forecasting, pushing the boundaries of existing computational paradigms.
Improving time series forecasting through more efficient and specialized AI architectures has significant implications for fields ranging from finance and logistics to climate prediction and autonomous systems.
The development of structure-guided sparse models like AME-TS can lead to more accurate, stable, and computationally efficient time series predictions by better handling data heterogeneity than current dense models.
- · AI researchers and developers
- · Companies reliant on predictive analytics
- · Cloud computing providers (through efficient model deployment)
- · Sectors with complex time series data (e.g., finance, supply chain)
- · Inefficient dense AI model architectures
- · Organizations with limited compute for large-scale forecasting
- · Current standard time series forecasting methods
More accurate and scalable time series forecasting models become widely available.
Improved predictive capabilities lead to optimized operations, resource allocation, and risk management across various industries.
The widespread adoption of specialized, efficient AI models could accelerate the development of more complex and autonomous decision-making systems.
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Read at arXiv cs.LG