Time Series Model for Monthly Airline Passengers Using Python

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a time series prediction model and document the end-to-end steps using a template. The Monthly Airline Passengers dataset is a time series situation where we are trying to forecast future outcomes based on past data points.

INTRODUCTION: The problem is to forecast monthly airline passenger volume. The dataset describes the monthly number of airline passengers in thousands from 1949 to 1960, and there are 144 observations. We used the first 75% of the observations for training and testing various models, while holding back the last 25% of the observations for validating the final model.

ANALYSIS: The baseline prediction (or persistence) for the dataset resulted in an RMSE of 50.30. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (2, 1, 1) with the seasonal order being (2, 1, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 14.74, which was better than the naive model as expected.

CONCLUSION: For this dataset, the chosen ARIMA model achieved excellent overall results and should be considered for further modeling.

Dataset Used: Monthly Totals of International Airline Passengers

Dataset ML Model: Time series forecast with numerical attributes

Dataset Reference: https://www.alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_airpassengers.html

The HTML formatted report can be found here on GitHub.