Parameter-Efficient Adaptation of Time Series Foundation Models for Electrical Load Forecasting

arXiv:2610.28200 · cs.LG, cs.AI · Submitted 2026-10-20 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-10-20

Updated: 2026-10-20

Comments: Accepted for presentation at the IEEE Power & Energy Student Summit (PESS 2026), Karlsruhe, Germany. 6 pages, 4 figures, 1 table

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 1× in this paper · explained in 1 episode
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks