Macroeconomic Forecasting with Large Language Models
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916965111562240 |
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| author | Carriero, Andrea Pettenuzzo, Davide Shekhar, Shubhranshu |
| author_facet | Carriero, Andrea Pettenuzzo, Davide Shekhar, Shubhranshu |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00890 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Macroeconomic Forecasting with Large Language Models Carriero, Andrea Pettenuzzo, Davide Shekhar, Shubhranshu Econometrics Computation and Language Machine Learning 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 |
| title | Macroeconomic Forecasting with Large Language Models |
| topic | Econometrics Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.00890 |