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
Cite This	Novel ML Approaches for Treasury Forecasting - A Literature Survey - Atharv Joshi - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.34679
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.34679
Short DOI	https://doi.org/g82hd7

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