SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
arXiv:2609.28582v1 Announce Type: cross Abstract: 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 inheren…
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Timeline · 2 reports
- 2026-09-25 04:00 · arXiv cs.LG
Time-Series Foundation Models That Understand Data Revisions - 2026-09-25 04:00 · arXiv stat.ML
SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models