Date of Award
9-9-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Tokunbo Ogunfunmi
Abstract
While machine learning has revolutionalized many fields such as natural language processing (NLP) and computer vision, its impact on time series forecasting is still widely disputed. This thesis focuses on comparing forecasting performance between econometrics/time series analysis, classical machine learning, and deep learning methods. More specifically, it is recursive multistep forecasting of the U.S. Treasury daily yield curve. The U.S Treasury daily yield curve is important because it is considered a leading indicator of recessions. While the majority of yield curve forecasting research focuses on monthly data, we are focusing on business daily data. We have also included a wide variety of methods that have not been tested on yield curve forecasting, especially deep learning algorithms. The algorithms include the Autoregressive Integrated Moving Average (ARIMA) model and its extensions, naive benchmarks, ensemble methods, Recurrent Neural Networks (RNNs), and multiple transformers built for forecasting. The thesis finds that ARIMA and naive benchmarks outperform all other models overall, except in one time period. Of the machine learning methods, TimeGPT and RNNs perform the best. Furthermore, the thesis explores whether stationary or nonstationary outputs are more appropriate as input to deep learning models.
Recommended Citation
Singh, Aman, "Recursive Multistep Forecasting of U.S. Treasury Yield Curves Using Machine Learning and Econometrics" (2024). Electrical and Computer Engineering Master's Theses. 9.
https://scholarcommons.scu.edu/elec_mstr/9