← Back to all news
arXiv Machine Learning By Zepei Cao

What Does History Add? Calendar Controls and Information-Timing Audits for University Trajectory Forecasting

Read the original on arXiv Machine Learning →

The study audits the predictive value of historical summaries in a rule-based university trajectory simulator. Seventeen ridge specifications and persistence models compare current plans, past-state and past-plan summaries, and calendar-only history-length indicators across training, validation, and two family-held-out evaluations, reconstructing 16,750 routes and 804,000 monthly records. Results show modest RMSE improvements with six-month history versus current-state prediction, but negligible differences from a length-only control, with uncertain generalization to all-history summaries and notable violations in fatigue predictions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Sep 4

RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting

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
ragbenchmarks
More like this →
arXiv Machine Learning
Sep 25

Time-Series Foundation Models That Understand Data Revisions

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
More like this →