arXiv (All)
open access
Reduced-Rank Autoregressive Models for Matrix Time Series
Matrix time series is a series of matrix data observed over time. Analytical tools for such time series is needed in many applications in finance, economics, engineering and many other fields. To avoid the use of vectorization of the matrices which loses the column and row information, and the vecto
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
- A Preliminary Estimation of the Economic Costs of Lockdown in Rio Grande do Sul#962281
- MomentQuant: an even more minimalist interval method with linear time complexity for time series classification#972085
- A Simple Note on Augmented Autoregressive Distributed Lag Model (A-ARDL)#246258
- The Infusion of Matrices into Statistics#493493
- High-Dimensional Matrix Factor Models for Count-Valued Time Series#413185
Record · ID 1076497
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