Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia

Abstract

On March 11,2020, the World Health Organization (WHO) declared Covid-19 as a global pandemic. The spread of Covid-19 has threatened many lives in nearly every country. In Malaysia, the health authorities have expressed concerns over an increasing number of cases and deaths. Due to the lockdown, this pandemic has also had an impact on most economic activities. Consequently, it is crucial to develop a reliable forecasting model to anticipate the number of cases. This study proposes two models: Autoregressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MPNN) in predicting the number of Covid-19 cases in Malaysia. Using Mean Absolute Error (MAE), the effectiveness and forecasting accuracy of the two models are compared and assessed. The lowest the value of MAE, the more accurate the forecasted outputs. The secondary data used in this study was the average number of Covid-19 cases each day in Malaysia from March 1, 2020, to March 29, 2021. To evaluate the data, RStudio and Alyuda NeuroIntelligence are utilised. As a consequence, the ARIMA (4,1,5) model provided the best fit to the data when compared to other ARIMA models, with a Mean Absolute Error (MAE) score of 1096.799. However, Multilayer Perceptron Neural Network (MPNN), which had the lowest MAE value of 334.591, outperformed ARIMA in terms of performance. The MPNN model was then used to forecast the number of Covid-19 instances for the next 30 days. According to the findings, daily increases in cases are anticipated.

Keywords

• ARIMA
• Multilayer Peceptron Neural Network
• Time-Series forecasting
• COVID-19
• Autoregressive Integrated Moving Average