arXiv (All)
open access
FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization
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
- HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling#1131662
- Memory in Deep Time-Series Models#973617
- Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors#266188
- Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting#1128900
- Progressive Memory Transformer: Memory-Aware Attention for Time-Series#1106050
Record · ID 1104928
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