aiqbench analysis
This research rigorously benchmarks Large Language Models against traditional econometric methods using the FRED-MD database to determine their viability in macroeconomic time series forecasting. The study critically evaluates whether LLMs can effectively capture intricate economic patterns or if conventional statistical approaches remain superior for this specific domain. By isolating performance metrics, it clarifies the practical boundaries of applying generative AI models to high-stakes financial prediction tasks without overhyping their capabilities.
Macroeconomic Forecasting with Large Language Models
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. 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
Abstract: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
Econometrics (econ.EM); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2407.00890 [econ.EM]
(or arXiv:2407.00890v5 [econ.EM] for this version)
https://doi.org/10.48550/arXiv.2407.00890 Focus to learn more arXiv-issued DOI via DataCite
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