AI Research
SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
Improving the reliability of long-term forecasting through advanced uncertainty quantification techniques.
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 ...
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ArXiv CS.LG
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ArXiv CS.LG
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SGA enhances time series foundation models by providing reliable confidence intervals for long-term predictions, which is critical for high-stakes decision-making. This advancement bridges the gap between raw predictive performance and the practical need for robust risk assessment in automated systems.
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