2 papers
stat.ML2026
Latent Inference-Time Guidance of Time Series Foundation Models
Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi +3
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 the…
stat.ME2026
Balanced Twins: Causal Inference on Time Series with Hidden Confounding
Maha Ouali, Badih Ghattas, Emmanuel Flachaire +2
Accurately estimating treatment effects in time series is essential for evaluating interventions in real-world applications, especially when treatment assignment is biased by unobs…