Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia

Abstract

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.