arXiv (All)
open access
A Unified Scaling Law for Time Series Foundation Models
We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support forecasting. Across different lookback lengths and forecast horizons, we analyze 18,768 experimental cells from 21 checkpoints on 23 dataset-frequency tas
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
- HART: Hybrid Attention-RWKV Time series Foundation Model#1296890
- TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models#1356170
- Evaluating Accuracy and Probabilistic Reliability of Zero-Shot Time Series Foundation Models#1061419
- When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events#1144353
- SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models#1076238
Record · ID 1201282
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