A Comparative Study between Univariate and Multivariate Time Series Models for COVID-19 Forecasting

Abstract

COVID-19, a disease produced by the SARS-CoV-2 virus, has had and continues to have a major influence on humankind. This pandemic has wreaked havoc on the global economy, pushing governments to take drastic steps to control its spread. Forecasting the growth of COVID-19 can assist healthcare providers, policymakers, manufacturers, and merchants predict the pandemic’s recurrence and the general public to have faith in the decisions made by them. Various existing findings showed that time-series techniques could learn and scale to properly anticipate how many people would be harmed by Covid-19 in the future. In this research, we did a comparative analysis of univariate time series models and multivariate time series models for confirming a better model at the end. As a result, we aim to bring out a time series model that is more suitable for forecasting the progression of pandemics worldwide, thus being a more reliable model. The research results showed that multivariate time series forecasting produced much better results for long-range than univariate time series models, which showed better results when expecting shorter periods.