← Back to all news
arXiv Machine Learning By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar

Macroeconomic Forecasting with Large Language Models

Read the original on arXiv Machine Learning →

arXiv:2407. 00890v5 Announce Type: replace-cross Abstract: This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches.

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.

Related stories

arXiv AI
Sep 1

Can LLMs Take the Pulse of the Economy? A Real-Time Evaluation of LLM Nowcasts on Macroeconomic Indicators

The paper introduces LiveMacroEval, a live benchmark that tests large language model (LLM) agents’ ability to produce hourly nowcasts for sixteen major U.S. macroeconomic indicators before their official release. It evaluates LLM performance against institutional nowcasts, Bloomberg ECOS consensus, and an auto-ARIMA baseline using a LiveMacro Score linked to announcement-window equity returns and a LiveBetting Score from simulated Polymarket-style trading. Over six months, state-of-the-art LLMs with web search achieved overall accuracy comparable to professional benchmarks, though performance varied across indicators.

By Xinyue Zhao, Ruiyi Zhang, Liqin Ye, Rui Cao, Pengtao Xie, Sudheer Chava
llmsagentsbenchmarks
More like this →
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
llmsnlpfine-tuning
More like this →
arXiv AI
Aug 25

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng
llmsragagentsbenchmarks
More like this →