arXiv (All)
open access
NIRVAR: Network Informed Restricted Vector Autoregression
High-dimensional panels of time series often arise in finance and macroeconomics, where co-movements within groups of panel components occur. Extracting these groupings from the data provides a coarse-grained description of the complex system in question and can inform subsequent prediction tasks. W
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
- When Does Forecasting Reveal Temporal Structure? A Stability Analysis of Time-Series Structural Selection#652444
- When Does Forecasting Reveal Temporal Structure? A Stability Analysis of Time-Series Structural Selection#650991
- Reduced-Rank Network Autoregression with Grouped Edge Effects#821763
- Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering#1132074
- Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds#1011616
Record · ID 1049580
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