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 models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

“…Typically, the adoption of forecasting techniques hinges on their accuracy and efficiency in handling data. This study finds its primary motivation in the advancement of hybrid forecasting models, with efficiency, accuracy, and precision serving as central themes in prior research and garnering significant attention in various scholarly publications [18][19][20][21][22][23].…”

“…It is expected that the new hybrid models derived from this study will be able to predict future Coronavirus outbreaks. Modeling COVID-19 with accurate prediction is important for tracking current reductions [20] and future progress [21,22] and evaluating countries' performance related to COVID-19 cases [23,24]. As far as our understanding goes, there have been scientific investigations related to the COVID-19 pandemic's spread using a hybrid model that incorporates both logistic regression (LR) and support vector machine (SVM).…”