Latent Inference-Time Guidance of Time Series Foundation Models
Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on in-context learning to make their predictions, which makes the quality of their performance highly sensitive to the user-selected lookback, covariates, horizon and training data distributions. In practise, the quality of the forecasts are variable but complementary, which highlights the need for a principled ensembling approach, rather than selecting the best context. This paper introduces Latent Inference-Time Guidance for TSFMs, which adaptively combines a pool of TSFM forecasts through a time-dependent latent space with independent compone...
| Authors | Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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