Analyzing the Complexity of US Federal Debt: A Mathematical Approach

Abstract

The United States federal debt has witnessed a significant surge over recent decades. This study delves into inquiries regarding the persistent patterns in federal debt, key factors driving this alarming trend, and the optimal timing for implementing corrective measures to mitigate its speeding flight. Utilizing modern machine learning techniques, notably Random Forest (RF) and Support Vector Regression (SVR), alongside conventional statistical forecasting techniques, the research aims to predict future trends. It emphasizes the critical role of business analytic thinking in deciphering fiscal system-based complexities. To address the mounting challenges, these research findings underscore the urgent necessity for efficacious policies to oversee them.