arXiv (OAI Expanded)
open access
Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection
astrophysics-of-galaxieschaotic-dynamicscomputational-physicsdata-analysisfluid-dynamicsstatistics-and-probability
Space-Time Projection (STP) is introduced as a data-driven forecasting approach for high-dimensional and time-resolved data. The method computes extended space-time proper orthogonal modes from training data spanning a prediction horizon comprising both hindcast and forecast intervals. Forecasts are
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
- Latent Inference-Time Guidance of Time Series Foundation Models#1142356
- Latent Inference-Time Guidance of Time Series Foundation Models#1122298
- From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting#1119057
- LTV-CTDNet: Compositional Turning Decomposition for Short-Term Turning-Movement Forecasting#1119087
- Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling#1035399
Record · ID 1084003
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