Predicting COVID-19 Confirmed Case in Surabaya using Autoregressive Integrated Moving Average, Bivariate and Multivariate Transfer Function

Abstract

In March 2020, the first case of Covid-19 was found in Indonesia. The increase of confirmed, suspected, and exposed in Surabaya has also significantly. Some studies show there is a relation among temperature, humidity, suspected, and exposed patients in an area with the number of confirmed COVID-19. Several statistical techniques that can be used to determine this relationship are to analyze and predict it using the ARIMA, bivariate, and multivariate transfer functions. The aim of this study is the performance of three models and determine the best model. The performance on the training data for ARIMA is 0.376, which shows that the accuracy of the model is 37.6%. The bivariate transfer function accuracy is 0.409, and the accuracy of the multivariate transfer function is 0.478. The result performance of ARIMA testing is 0.074, the bivariate transfer function is 0.055, and the multivariate transfer function is 0.108. The multivariate transfer function forecasting model is a technique in this case with the best performance.