Developed Unobserved Components Model (UCM) support for the Nixtla/statsforecast repository, enabling users to decompose time series data into level, trend, seasonal, and cycle components for more granular forecasting and analysis. The implementation focused on clean API design and robust commit traceability, ensuring maintainability and ease of integration. Leveraging Python for statistical modeling and time series analysis, the work emphasized unit testing to maintain code quality and reliability. No major bugs were reported during this period, reflecting a careful and methodical approach to feature delivery. The contribution expanded the library’s forecasting capabilities for users requiring detailed interpretability.