Machine Learning-Powered Financial Forecasting in the U.S. Tourism Industry: Predicting Market Trends and Consumer Spending with Artificial Intelligence.

Abstract

The tourism industry in the United States is a significant driver of economic growth, contributing substantially to GDP and employment. In an increasingly dynamic market, machine learning (ML) has emerged as a powerful tool for financial forecasting, enabling more accurate predictions of market trends and consumer spending patterns (Law et al., 2022). This study explores the role of ML-powered financial forecasting in the U.S. tourism sector, analyzing its effectiveness in predicting fluctuations in consumer spending, seasonality trends, and demand forecasting (Chen et al., 2020). Leveraging supervised and unsupervised learning algorithms, ML models process vast datasets, including economic indicators, social media sentiment, and historical transaction data, to enhance predictive accuracy (Guo et al., 2019). This paper discusses key machine learning techniques such as neural networks, regression models, and time series analysis, examining their applicability and limitations in forecasting financial trends in tourism (Makridakis et al., 2018). Additionally, ethical considerations and data privacy concerns in AI-driven predictions are explored (Xiao & Smith, 2021). The findings suggest that ML models significantly enhance financial forecasting accuracy compared to traditional statistical methods, providing valuable insights for businesses, policymakers, and stakeholders in the tourism industry (Buhalis & Volchek, 2021). Future research directions include integrating deep learning frameworks and real-time data analytics to further refine predictive capabilities (Good fellow et al., 2016).