Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model.

Autor:	Khan DM; Department of Statistics, Abdul Wali Khan University Mardan, Mardan, Pakistan., Ali M; Department of Statistics, Abdul Wali Khan University Mardan, Mardan, Pakistan., Iqbal N; Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan.; Division of Computer Science, Mathematics and Science, St John's University, New York, NY, United States., Khalil U; Department of Statistics, Abdul Wali Khan University Mardan, Mardan, Pakistan., Aljohani HM; Department of Mathematics & Statistics, College of Science, Taif University, Taif, Saudi Arabia., Alharthi AS; Department of Mathematics & Statistics, College of Science, Taif University, Taif, Saudi Arabia., Afify AZ; Department of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt.
Jazyk:	angličtina
Zdroj:	Frontiers in public health [Front Public Health] 2022 Jul 29; Vol. 10, pp. 922795. Date of Electronic Publication: 2022 Jul 29 (Print Publication: 2022).
DOI:	10.3389/fpubh.2022.922795
Abstrakt:	In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky-Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Khan, Ali, Iqbal, Khalil, Aljohani, Alharthi and Afify.)
Databáze:	MEDLINE
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