Time Series Forecasting on COVID-19 Data and Its Relevance to International Health Security

Abstract

The Corona virus pandemic is the most tragic virus outbreak in more than a century. Corona has globally already taken the lives of four million people, across all continents. The virus has the potential to become very catastrophic, if a significant part of the world population does not have any form of immunity against it. In this project, the aim is to make forecasts on the number of daily infections in the Netherlands. Seven different models were implemented to forecast the number of infected people in a three-month time period. The sequential CNN model outperformed all other models substantially. The capabilities of CNN models in time series forecasting can be very encouraging in conducting more research on time series data with convolutional neural networks.

Forecasting the health expenditures in Iran by Using Time Series Analysis as Machine Learning Model in Python

“…If the PACF plot shows serious data spikes in the beginning, and in later lags, but only very few spikes in the ACF plot, then it is recommended to use the MA model, and make use of the MA component (q > 0) in the ARIMA model. If cases when both charts show many significances, it is recommended to model the data with an ARMA or ARIMA model [31].…”

“…So, the AR model and the MA model were performed, and it predicted the future. Finally, we converted the dataset into the cumulative sum and plotted the graph [31].…”