AI-Driven Predictive Modeling of US Economic Trends: Insights and Innovations

Abstract

While traditional economic forecasting models have significantly improved, a few significant obstacles must be overcome. Conventional methods include time series and econometric models, which may rely hugely on historical data and are underlain by linear assumptions. These models fail to handle sudden shifts in the market or unseen economic events, like the financial crisis of 2008 or the COVID-19 pandemic 2020. Traditional models also need help bottling the vast amounts of unstructured data available today: articles, social posts, and other real-time information streams that influence economic sentiment. The chief objective of this research project is to explore the efficacy of AI-driven predictive modeling in forecasting US economic trends. This research project involved a time series analysis of three key financial indicators: Most notably, the Consumer Price Index (CPIAUCSL), the Gross Domestic Product (GDP), and the Unemployment Rate (UNRATE). Datasets entailed the Consumer Price Index for All Urban Consumers (CPI) from 1950 to the present, GDP: U.S. Gross Domestic Product (GDP) quarterly, and UNRATE: US Unemployment Rate (UNRATE) from 1950 to the present. These datasets provided valuable insights into the US economy, and this analysis aims to explore trends, seasonality, and relationships between these variables over time. One of the most immediate benefits for policymakers and the U.S. government is the significant improvement in the accuracy and timeliness of economic forecasts enabled by AI-driven models such as the ARIMA and SARIMAX. Another vital implication of AI-driven economic forecasting is improving policy formulation based on more sophisticated scenario analysis and simulations. AI-driven forecasting facilitates more targeted and proactive policy intervention, which is helpful in such sector-specific issues or regional disparities in economic performance.