Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022.

ABSTRACT: Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM m

SUBMITTER: Wan Mohamad Nawi WIA 

PROVIDER: S-EPMC10180663 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature