mlsanityData to 7 Oct 2026

Macroeconomic Forecasting with Large Language Models

arXiv 2407.00890 ↗First posted 1 Jul 2024econ.EM cs.CL cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to conventional methods remains an area of interest. To address this, we conduct a rigorous evaluation of LLMs against traditional macro forecasting methods, using as common ground the FRED-MD database. Our findings provide valuable insights into the strengths and limitations of LLMs in forecasting macroeconomic time series, shedding light on their applicability in real-world scenarios

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 61 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

22 of 61 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  6. 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 722
  7. 2024Chronos: Learning the Language of Time Seriescited by 712
  8. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  9. 2023A decoder-only foundation model for time-series forecastingcited by 650
  10. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
  11. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  12. 2022Transformers in Time Series: A Surveycited by 489
10 more
  1. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  2. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  3. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 170
  4. 2023TimeGPT-1cited by 149
  5. 2023TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecastingcited by 136
  6. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
  7. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  8. 2023LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Predictioncited by 19
  9. 2020Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARscited by 11
  10. 2020Inference in Bayesian Additive Vector Autoregressive Tree Modelscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025STEER-ME: Assessing the Microeconomic Reasoning of Large Language Modelscited by 7
  2. 2025Macroeconomic Forecasting and Machine Learningcited by 1
  3. 2026DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networkscited by 1
  4. 2025Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysiscited by 0
  5. 2025A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributionscited by 0
  6. 2026Quantifying the Risk-Return Tradeoff in Forecastingcited by 0