Forecasting the Confirmed COVID-19 Cases Using Modal Regression

This study utilizes modal regression to forecast the cumulative confirmed COVID-19 cases in Canada, Japan, South Korea, and the United States. The objective is to improve the accuracy of the forecasts compared to standard mean and median regressions. To evaluate the performance of the forecasts, we conduct simulations and introduce a metric called the coverage quantile function (CQF), which is optimized using modal regression. By applying modal regression to popular time-series models for COVID-19 data, we provide empirical evidence that the forecasts generated by the modal regression outperform those produced by the mean and median regressions in terms of the CQF. This finding addresses the limitations of the mean and median regression forecasts.