COVID-19 is a viral infection caused by a recently identified coronavirus that has impacted the lives of millions of people worldwide. In Malaysia, the number of COVID-19 cases has been increasing since 2021. This study aims to find the best model for forecasting the number of new confirmed cases of COVID-19 in Malaysia by comparing Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series models. ARIMA is commonly used for time-series analysis, forecasting, and control, while Fuzzy Time Series provides an alternative approach for predicting COVID-19 outbreaks. The error measures used to compare the models include Mean Square Error, Root Mean Square Error, and Mean Absolute Percentage Error. The study's results demonstrate that the Fuzzy Time Series model has the smallest error measure values compared to ARIMA, indicating that it is more accurate.
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