arXiv (All)
open access
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ. The number of targets and covariates changes from dataset to dataset, horizons vary by orders of magnitude, and sampling rates range from secon
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
- Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories#1234361
- HART: Hybrid Attention-RWKV Time series Foundation Model#1296890
- Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting#1035319
- WorldCast: Exploring Intelligent Visual Generation in World State Prediction#1355720
- Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards#1357450
Record · ID 1388887
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