arXiv (All)
open access
A Later Test Set Is Not a New Domain: Pretraining Familiarity Survives a Contamination-Free Hold-Out
Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having seen the test set during pretraining. The obvious remedy is a hold-out that postdates the models. We build one: thirteen forecasters -- four classica
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Does Training on Future Data Pay? Look-Ahead Bias in Forecasting with Pretrained Models#1034367
- Time-Series Foundation Models That Understand Data Revisions#1076232
- Loss-Guided Pretraining Data Selection for Time-Series Foundation Models#1141299
- Post-Training in Time Series Foundation Models: A Unifying Framework#1011169
- QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization#1048806
Record · ID 997247
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive