Time Series Model for Australian Total Wine Sales 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 Total Wine Sales 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 wine sales in Australia. The dataset describes a time-series of wine sales by Australian winemakers between Jan 1980 – Aug 1994, and there are 176 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 7532. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (1, 1, 2) with the seasonal order being (0, 1, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 2907, which was better than the baseline model as expected.

CONCLUSION: For this dataset, the chosen ARIMA model achieved a satisfactory result and should be considered for further modeling.

Dataset Used: Monthly Australian Total Wine Sales

Dataset ML Model: Time series forecast with numerical attributes

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

The HTML formatted report can be found here on GitHub.