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SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predictions over extended future horizons. However, existing TSFMs often suffer from significantly inherent uncertainty, which typically manifests as derived
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