arXiv (All)
open access
Scale-Invariant Training for Time Series Foundation Models
Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setting exposes models to series whose scales -- typical magnitudes of their values -- can differ substantially. Affine scaling methods such as Reversible I
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
- Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting#1116370
- Distillation of Synthetic Data for Time Series Foundation Models#1084402
- Fracast-0: Fractal Weight Sharing for a Time Series Foundation Model with Only 85K Parameters#1116540
- SIFT: Enhancing Time Series Foundation Models via Semantic Invariance and Structural Fidelity Fine-Tuning#1117007
- Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models#1129119
Record · ID 1216699
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