arXiv Machine Learning June 9, 2026 By Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel

Zero and Few Shot Load Forecasting with Large Language Models

Read the original on arXiv Machine Learning →

arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

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arXiv Machine Learning

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llms benchmarks

arXiv Machine Learning

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llms benchmarks

arXiv AI

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Related stories

Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting