Ryerson Course Project This paper looks at effectively forecasting COVID-19 cases in Canada from 2019 to 2021 by using time series machine learning methods. The aim is to test the effectiveness, efficiency and stability of the models used to better understand how these algorithms function as well as their pros and cons. There are several research questions that will provide insight on time series models with the first being how easily a time series model can be developed using raw real-world data. The second question will explore how far forecasts should be set to and the consequences of going too far. The third question will look at external factors that affect forecasting. Several peer reviewed articles are used to build a foundation of knowledge regarding this topic before proceeding into the analytical models that were built for this paper. The models used in this paper range from a linear regression model to SARIMA model to forecast positive COVID-19 cases in Canada from data containing all the recorded cases in Canada from 2020 to present day.
The contents of this repository contain:
COVID-19 Sarima Model for Canada
Data Exploration Folder -This is where a Linear Regression model and a Stationary Testing model are located.
Dataset Link: https://open.canada.ca/data/en/dataset/b8d1d622-1ceb-4c1c-96e9-a0b38939080b