Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction
Mashael Khayyat, Kaouther Laabidi, Nada Almalki, Maysoon Al-zahrani
Abstract
COVID-19 comes from a large family of viruses identified in 1965; to date, seven groups have been recorded which have been found to affect humans. In the healthcare industry, there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict confirmed cases, recovered cases, and deaths. Many researchers and scientists in the field of machine learning are also involved in solving this dilemma, seeking to understand the patterns and characteristics of virus attacks, so scientists may make the right decisions and take specific actions. Furthermore, many models have been considered to predict the Coronavirus outbreak, such as the retro prediction model, pandemic Kaplan’s model, and the neural forecasting model. Other research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various countries. Thus, we proposed a prediction and analysis model to predict COVID-19 in Saudi Arabia. The time series dependent face book prophet model is used to fit the data and provide future predictions. This study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia, using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or weekly. We found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 dataset. In contrast, the proposed model of death cases has a high ability to forecast the COVID-19 dataset. Finally, obtaining more data could empower the model for further validation.