JointFM is a zero-shot foundation model for joint distributional prediction of coupled time series, trained on synthetic SDEs and reducing energy loss by 21.1% versus the strongest baseline on unseen synthetic oracles.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
R-NP model uses DS-HDP-HMM regime detection plus per-regime CNPs to produce regime-weighted price forecasts that rank as the most balanced option under TOPSIS across 2021-2023 when tested in battery arbitrage and grid-service tasks.
citing papers explorer
-
JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction
JointFM is a zero-shot foundation model for joint distributional prediction of coupled time series, trained on synthetic SDEs and reducing energy loss by 21.1% versus the strongest baseline on unseen synthetic oracles.
-
Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting
R-NP model uses DS-HDP-HMM regime detection plus per-regime CNPs to produce regime-weighted price forecasts that rank as the most balanced option under TOPSIS across 2021-2023 when tested in battery arbitrage and grid-service tasks.