  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
