arXiv (All)
open access
XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching
Future exogenous variables provide valuable information for forecasting endogenous time series. Existing covariate-aware methods primarily learn the direct influence of exogenous variables on endogenous variables. However, these effects can be complex and change with the pattern of the exogenous var
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
- Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting#1128900
- Latent Inference-Time Guidance of Time Series Foundation Models#1142356
- Latent Inference-Time Guidance of Time Series Foundation Models#1122298
- A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters#1035014
- Multivariate Time Series Forecasting needs Cross Variable Loss#1118677
Record · ID 1129547
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