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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374278/ https://www.ncbi.nlm.nih.gov/pubmed/35968475 http://dx.doi.org/10.3389/fpubh.2022.922795 |
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author | Khan, Dost Muhammad Ali, Muhammad Iqbal, Nadeem Khalil, Umair Aljohani, Hassan M. Alharthi, Amirah Saeed Afify, Ahmed Z. |
author_facet | Khan, Dost Muhammad Ali, Muhammad Iqbal, Nadeem Khalil, Umair Aljohani, Hassan M. Alharthi, Amirah Saeed Afify, Ahmed Z. |
author_sort | Khan, Dost Muhammad |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9374278 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93742782022-08-13 Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model Khan, Dost Muhammad Ali, Muhammad Iqbal, Nadeem Khalil, Umair Aljohani, Hassan M. Alharthi, Amirah Saeed Afify, Ahmed Z. Front Public Health Public Health 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. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9374278/ /pubmed/35968475 http://dx.doi.org/10.3389/fpubh.2022.922795 Text en Copyright © 2022 Khan, Ali, Iqbal, Khalil, Aljohani, Alharthi and Afify. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Khan, Dost Muhammad Ali, Muhammad Iqbal, Nadeem Khalil, Umair Aljohani, Hassan M. Alharthi, Amirah Saeed Afify, Ahmed Z. Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and 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 | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374278/ https://www.ncbi.nlm.nih.gov/pubmed/35968475 http://dx.doi.org/10.3389/fpubh.2022.922795 |
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