International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Novel ML Approaches for Treasury Forecasting - A Literature Survey

Author(s) Atharv Joshi
Country United States
Abstract U.S. government bonds are affected by central bank decisions. Bonds are less easily traded than stocks and the public data about them are not abundantly avail- able. In this project, we review the state-of-the-art methods in machine learning (ML) and artificial intelligence (AI) methods employed in forecasting interest rates for U.S. treasuries of varying maturities. Our work will highlight how powerful AI techniques can be leveraged in more accurate predictions of movement in treasury/government bonds.
Keywords ARIMA, XGBoost, Autoformer, Treasury
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-10
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.34679
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E-ISSN 2582-2160
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