arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
By Jiachen Cong, Jingbo Liu
Rolling Conformal Prediction (rolling‑CP) is a distribution‑free predictive inference method designed for sequential model training. It calibrates each incoming observation against the current predictor and incorporates it into future training, eliminating the need for data splitting. For exchangeable data, rolling‑CP guarantees marginal coverage with a universal factor‑two bound, and for i.i.d. streams it provides high‑probability training‑conditional validity over time, improving to the target level under stability conditions.
By Chen Cheng, Ruiting Liang, Rina Foygel Barber
SPACE is a conformal wrapper that creates ellipsoidal joint prediction regions for multivariate time‑series forecasts by estimating time‑local covariance directly from the current forecast sample cloud. It calibrates the region’s radius using a dynamic backward window‑selection scheme, avoiding reliance on historical residuals. Experiments on diverse datasets show that SPACE improves joint and rolling coverage, achieving better coverage‑efficiency tradeoffs than existing wrappers.
By Baishi Li, Kelvin J. L. Koa, Ke-Wei Huang
arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.
By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar
arXiv:2602. 16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima.
By Xu Zhang, Peng Wang, Yichen Li, Wei Wang
The paper critiques the prevalent use of mean squared error (MSE) for evaluating irregular time‑series forecasting, arguing that MSE is biased by timestamp sampling distributions. It introduces the Continuous‑time Squared Error (CSE), an importance‑weighted metric that theoretically offers a tighter asymptotic bound on continuous‑time risk than MSE. A comprehensive benchmark across synthetic, semi‑synthetic, and eight real‑world datasets demonstrates that CSE more accurately recovers continuous‑time risk, revealing limitations of relying solely on MSE.
By Rongwen Li, Haixin Xie, Xiao Wang, Changjian Chen