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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Sep 10

Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

arXiv:2609.06656v1 Announce Type: cross Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...

By Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani

fine-tuning benchmarks

arXiv Machine Learning

Jul 28

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

arXiv:2607. 23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training.

By Morad Laglil, Bertrand Pracca, Emilie Devijver, Eric Gaussier

llms nlp fine-tuning

arXiv Machine Learning

Aug 21

Systematic Evaluation of TabPFN-TS for Zero-Shot Probabilistic Heat Load Forecasting in District Heating Networks

arXiv:2608. 20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling.

By Ben Spoek, Karim K. Ben Hicham, Kai Derzsi, Philipp Althaus, Alexander Mitsos, Dirk M\"uller

arXiv Machine Learning

2d ago

When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events

arXiv:2609.39386v1 Announce Type: new Abstract: Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on whi...

By Daniel Schoess, Florian von Wangenheim

benchmarks

arXiv Machine Learning

Sep 15

Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting

arXiv:2609.13840v1 Announce Type: new Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...

By Joo Ern Chin, Shih-Fen Cheng, Aldy Gunawan

benchmarks safety

arXiv AI

Jul 21

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

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

One email a morning, machine-written

Related stories

Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

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

Systematic Evaluation of TabPFN-TS for Zero-Shot Probabilistic Heat Load Forecasting in District Heating Networks

When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events

Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting

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