Papers / Updated at: 2026-10-31
Baron et al. (2025): Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Authors:
- Ethan Baron
- Boris Oreshkin
- Ruijun Ma
- Hanyu Zhang
- Kari Torkkola
- Michael W. Mahoney
- Andrew Gordon Wilson
- Tatiana Konstantinova
Source: https://arxiv.org/abs/2510.02224
DOI: https://doi.org/10.48550/arXiv.2510.02224
Mentioned in:
- Baron et al. (2025): Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
- Park et al. (2021): Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
- Shuffler
Simple approach to turn the common quantiles of marginal forecast distributions into sample paths from a join forecast distribution. This is important as many time series (foundation) models return only quantiles which can’t answer complex questions about a span of several time steps in the forecast horizon.
Uses the approach described in section 3.2 of Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting (Park et al., 2021) to evaluate arbitrary quantile levels by interpolating between the known quantiles.