Time Series Model for Australian Resident Population 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 Australian Resident Population 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 quarterly resident population in Australia. The dataset describes the numbers (in thousands) of Australian residents measured quarterly from March 1971 to March 1994, and there are 89 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 60.17. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (0, 2, 1) with the seasonal order being (0, 0, 0, 0). 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 a satisfactory result and should be considered for further modeling.

Dataset Used: Quarterly Totals of Australian Resident Population

Dataset ML Model: Time series forecast with numerical attributes

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

The HTML formatted report can be found here on GitHub.