In this postgraduate project, I investigated the predictability of annual trout stocking numbers in New York State to make informed recommendations. I acquired data from the NYSDEC and performed extensive data cleaning and preparation using Python and Pandas. This involved filtering the data for relevant trout species, handling missing values, and converting dates for time series analysis. The analysis centered on developing and evaluating a Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) model to forecast stocking numbers. I compared the SARIMAX model's performance to that of a simple average model for each species, utilizing metrics like Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error.