Skip to content

Similar papers

#machine learning Preprint Sep 2026

Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift

Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to covariate shift, concept shift, and temporal dependence. Probably Approximately Correct Bayesian domain adaptation provides computable certificates by decomposing target...

Chen-Feng Huang, Zi-Xuan Ma, George Michailidis · 3 citations
Preprint Aug 2026

Counterfactual Evaluation of Temporal Observation Protocols

We study counterfactual protocol evaluation: whether data collected under a realised observation protocol determine the predictive value of alternatives that were never deployed. Protocol value is the population $R^2$ of the Bayes-optimal predictor of a fixed trajectory-level target from the measurements an alternative...

Xi-Zhe Zhang · 0 citations
#machine learning Preprint Sep 2026

Time-Series Foundation Models That Understand Data Revisions

Historical observations are not always fixed: statistical agencies revise previously published values as new evidence arrives. Forecasting from a contemporary download can therefore expose a model to information unavailable at the date it purportedly made a prediction. We propose VINTAGE-TS, a revision-aware adaptation...

Taimoor Ahmad · 0 citations
Preprint Aug 2026

Let Time Tell: Identification and Gaussian Process Estimation for Interrupted Time Series

We study causal inference in interrupted time series designs where a treatment affects every unit simultaneously, so that the contemporaneous controls used by difference-in-differences and synthetic control are unavailable and the counterfactual must be extrapolated from a unit's own pre-treatment history. We establish...

S. Cho · 0 citations
#machine learning Preprint Sep 2026

ITSY: Causal Discovery From Irregular Time-Series Data

Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are missing. We introduce ITSY, the first continuous-optimization method for causal discovery from irregular time series under a linear model. I...

Wen-Bo Xu, Yue He, Yun-Hai Wang et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting

The Continuous-time Squared Error (CSE) is proposed, which employs importance weighting to eliminate the influence of the timestamp sampling distributions and theoretically proves that CSE's asymptotic estimation error with respect to continuous-time risk is no greater than that of MSE.

Rong Li, Hai-Xin Xie, Xiao Wang et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.