Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.922795/full |
| _version_ | 1818017295596781568 |
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| author | Dost Muhammad Khan Muhammad Ali Nadeem Iqbal Nadeem Iqbal Umair Khalil Hassan M. Aljohani Amirah Saeed Alharthi Ahmed Z. Afify |
| author_facet | Dost Muhammad Khan Muhammad Ali Nadeem Iqbal Nadeem Iqbal Umair Khalil Hassan M. Aljohani Amirah Saeed Alharthi Ahmed Z. Afify |
| author_sort | Dost Muhammad Khan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T07:25:14Z |
| format | Article |
| id | doaj.art-2ae7a1a5c2634713ae48b584353152de |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-14T07:25:14Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-2ae7a1a5c2634713ae48b584353152de2022-12-22T02:06:02ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-07-011010.3389/fpubh.2022.922795922795Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal ModelDost Muhammad Khan0Muhammad Ali1Nadeem Iqbal2Nadeem Iqbal3Umair Khalil4Hassan M. Aljohani5Amirah Saeed Alharthi6Ahmed Z. Afify7Department of Statistics, Abdul Wali Khan University Mardan, Mardan, PakistanDepartment of Statistics, Abdul Wali Khan University Mardan, Mardan, PakistanDepartment of Computer Science, Abdul Wali Khan University Mardan, Mardan, PakistanDivision of Computer Science, Mathematics and Science, St John's University, New York, NY, United StatesDepartment of Statistics, Abdul Wali Khan University Mardan, Mardan, PakistanDepartment of Mathematics & Statistics, College of Science, Taif University, Taif, Saudi ArabiaDepartment of Mathematics & Statistics, College of Science, Taif University, Taif, Saudi ArabiaDepartment of Statistics, Mathematics and Insurance, Benha University, Benha, EgyptIn 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.https://www.frontiersin.org/articles/10.3389/fpubh.2022.922795/fullpredictionCOVID-19ensemble empirical mode decompositionaugmented Dicky-Fuller testARIMAerror trend seasonal model |
| spellingShingle | Dost Muhammad Khan Muhammad Ali Nadeem Iqbal Nadeem Iqbal Umair Khalil Hassan M. Aljohani Amirah Saeed Alharthi Ahmed Z. Afify Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model Frontiers in Public Health prediction COVID-19 ensemble empirical mode decomposition augmented Dicky-Fuller test ARIMA error trend seasonal model |
| title | Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model |
| title_full | Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model |
| title_fullStr | Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model |
| title_full_unstemmed | Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model |
| title_short | Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model |
| title_sort | short term prediction of covid 19 using novel hybrid ensemble empirical mode decomposition and error trend seasonal model |
| topic | prediction COVID-19 ensemble empirical mode decomposition augmented Dicky-Fuller test ARIMA error trend seasonal model |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.922795/full |
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