The project forecasts quarterly tourism flows from Mexico to Canada using historical data from 1995 to 2019. Models that were used were Seasonal Naive, ETS, ARIMA, and regression with lagged tourism and GDP which were trained on data between 1995 – 2017, validated on data from 2018, and tested on data from 2019 to ensure robust forecasts. Forecast accuracy was assessed with metrics such as MAE, RMSE, and MAPE. ETS performed best on the validation set, ARIMA performed best on the test set, and regression highlighted the value of including GDP as a macroeconomic predictor. The analysis revealed strong seasonal patterns, with peaks in Q3 and declines in Q4, whereas forecasts indicated continued growth in arrivals for 2019. The exploration of the data and the modeling within the project was conducted with R in RStudio.