Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study.
Identifieur interne : 001157 ( Main/Exploration ); précédent : 001156; suivant : 001158Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study.
Auteurs : Abdelhafid Zeroual [Algérie] ; Fouzi Harrou [Arabie saoudite] ; Abdelkader Dairi [Algérie] ; Ying Sun [Arabie saoudite]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Gated recurrent units (GRUs) and Variational AutoEncoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms.
DOI: 10.1016/j.chaos.2020.110121
PubMed: 32834633
PubMed Central: PMC7362800
Affiliations:
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time-Series data: A Comparative study.</title><author><name sortKey="Zeroual, Abdelhafid" sort="Zeroual, Abdelhafid" uniqKey="Zeroual A" first="Abdelhafid" last="Zeroual">Abdelhafid Zeroual</name><affiliation wicri:level="1"><nlm:affiliation>Faculty of technology, Department of electrical engineering, University of 20 August 1955, Skikda 21000, Algeria.</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>Faculty of technology, Department of electrical engineering, University of 20 August 1955, Skikda 21000</wicri:regionArea><wicri:noRegion>Skikda 21000</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>LAIG Laboratory, University of 08 May 1945, Guelma 24000, Algeria.</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>LAIG Laboratory, University of 08 May 1945, Guelma 24000</wicri:regionArea><wicri:noRegion>Guelma 24000</wicri:noRegion></affiliation></author><author><name sortKey="Harrou, Fouzi" sort="Harrou, Fouzi" uniqKey="Harrou F" first="Fouzi" last="Harrou">Fouzi Harrou</name><affiliation wicri:level="1"><nlm:affiliation>Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900</wicri:regionArea><wicri:noRegion>23955-6900</wicri:noRegion></affiliation></author><author><name sortKey="Dairi, Abdelkader" sort="Dairi, Abdelkader" uniqKey="Dairi A" first="Abdelkader" last="Dairi">Abdelkader Dairi</name><affiliation wicri:level="3"><nlm:affiliation>University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), Computer Science department Signal, image and speech laboratory (SIMPA) laboratory, El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria.</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), Computer Science department Signal, image and speech laboratory (SIMPA) laboratory, El Mnaouar, BP 1505, Bir El Djir 31000, Oran</wicri:regionArea><placeName><settlement type="city">Oran</settlement><region nuts="2">Wilaya d'Oran</region></placeName></affiliation></author><author><name sortKey="Sun, Ying" sort="Sun, Ying" uniqKey="Sun Y" first="Ying" last="Sun">Ying Sun</name><affiliation wicri:level="1"><nlm:affiliation>Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900</wicri:regionArea><wicri:noRegion>23955-6900</wicri:noRegion></affiliation></author></analytic><series><title level="j">Chaos, solitons, and fractals</title><idno type="ISSN">0960-0779</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Gated recurrent units (GRUs) and Variational AutoEncoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834633</PMID><DateRevised><Year>2020</Year><Month>08</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>140</Volume><PubDate><Year>2020</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study.</ArticleTitle><Pagination><MedlinePgn>110121</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.110121</ELocationID><Abstract><AbstractText>The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Gated recurrent units (GRUs) and Variational AutoEncoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zeroual</LastName><ForeName>Abdelhafid</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Faculty of technology, Department of electrical engineering, University of 20 August 1955, Skikda 21000, Algeria.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>LAIG Laboratory, University of 08 May 1945, Guelma 24000, Algeria.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Harrou</LastName><ForeName>Fouzi</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dairi</LastName><ForeName>Abdelkader</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), Computer Science department Signal, image and speech laboratory (SIMPA) laboratory, El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Ying</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Data-driven</Keyword><Keyword MajorTopicYN="N">Deep learning</Keyword><Keyword MajorTopicYN="N">Forecasting</Keyword><Keyword MajorTopicYN="N">Gated recurrent units</Keyword><Keyword MajorTopicYN="N">Long short-term memory</Keyword><Keyword MajorTopicYN="N">Recurrent neural network</Keyword><Keyword MajorTopicYN="N">Variational autoencoder</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32834633</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.110121</ArticleId><ArticleId IdType="pii">S0960-0779(20)30518-X</ArticleId><ArticleId IdType="pii">110121</ArticleId><ArticleId IdType="pmc">PMC7362800</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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