This research offers a data-driven approach to analyzing Texas’s state budget for the 2024 fiscal year and forecasting future allocations across key sectors, including Health, Transportation, Education, and Public Safety. Employing Python’s Pandas library for extensive data processing and the Dash framework for dynamic visualization, we examine tax revenues and expenditures, derive per capita figures, and analyze spending patterns to highlight Texas’s financial priorities for 2024.
To project allocations beyond 2024, we develop a time series forecasting model using historical data (1998 – 2024), applying ARIMA,SARIMA and Exponential Smoothing models enhanced with lag features and economic indicators to estimate future budget needs across major categories.Model accuracy is evaluated with Mean Absolute Percentage Error on a 2025 hold-out set. The interactive dashboard presents a detailed view of the 2024 budget, while the forecasting model provides critical insights to support strategic fiscal planning for subsequent years. This study serves as a resource for policymakers, offering a robust foundation for anticipating resource needs in alignment with Texas’s economic landscape.