arXiv:2608. 10433v1 Announce Type: new Abstract: Forecast accuracy does not tell us which past inputs produced a prediction.
By Qipeng Qian, Yuntao Qian
arXiv:2608. 10433v4 Announce Type: replace Abstract: Time-series forecasters increasingly accompany numerical predictions with explicit temporal reports, such as delays or selected history, but a correct report need not describe the information actually used by the forecast.
By Qipeng Qian, Yuntao Qian
arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.
By Qipeng Qian, Yuntao Qian
The paper introduces VINTAGE-TS, a revision‑aware time‑series foundation model that separates observation time from information‑availability time. It predicts both the next period’s first‑published value and the value available after a fixed delay, maintaining a joint distribution to capture their dependence and uncertainty. The authors provide a detailed evaluation protocol, software tools for validity‑interval reconstruction and delayed‑label filtering, and a synthetic demonstration with a 25‑configuration sensitivity suite to illustrate performance variability and the impact of hindsight contamination.
By Taimoor Ahmad
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
RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.
By Yuchen He, Yueyang Cang, Zhiyuan Ning, Ningyu Wang, Li Shi