About: Study of ARIMA and Least Square Support Vector Machine (LS-SVM) Models for the Prediction of SARS-CoV-2 Confirmed Cases in the Most Affected Countries

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About: Study of ARIMA and Least Square Support Vector Machine (LS-SVM) Models for the Prediction of SARS-CoV-2 Confirmed Cases in the Most Affected Countries Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Study of ARIMA and Least Square Support Vector Machine (LS-SVM) Models for the Prediction of SARS-CoV-2 Confirmed Cases in the Most Affected Countries
Creator	Jitendra, Sidhu Kaur, Jatinder Kumar, Jatinder Makkhan, Singh Parmar, Singh Peshoria, Shruti Singh Parmar, Kulwinder Singh, Sarbjit
Source	Elsevier; Medline; PMC
abstract	Discussions about the recently identified deadly coronavirus disease (COVID-19) which originated in Wuhan, China in December 2019 are common around the globe now. This is an infectious and even life-threatening disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has rapidly spread to other countries from its originating place infecting millions of people globally. To understand future phenomena, strong mathematical models are required with the least prediction errors. In the present study, autoregressive integrated moving average (ARIMA) and least square support vector machine (LS-SVM) models are applied to the data consisting of daily confirmed cases of SARS-CoV-2 in the most affected five countries of the world for modeling and predicting one-month confirmed cases of this disease. To validate these models, the prediction results were tested by comparing it with testing data. The results revealed better accuracy of the LS-SVM model over the ARIMA model and also suggested a rapid rise of SARS-CoV-2 confirmed cases in all the countries under study. This analysis would help governments to take necessary actions in advance associated with the preparation of isolation wards, availability of medicines and medical staff, a decision on lockdown, training of volunteers, and economic plans.
has issue date	2020-07-04(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.110086
bibo:pmid	32834622
has license	no-cc
sha1sum (hex)	94cb10e55eeeb06713e8513eb1a6795abf47b29c
schema:url	https://doi.org/10.1016/j.chaos.2020.110086
resource representing a document's title	Study of ARIMA and Least Square Support Vector Machine (LS-SVM) Models for the Prediction of SARS-CoV-2 Confirmed Cases in the Most Affected Countries
has PubMed Central identifier	PMC7345281
has PubMed identifier	32834622
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:94cb10e55eeeb06713e8513eb1a6795abf47b29c#body_text
is schema:about of	named entity 'ARIMA' named entity 'SVM' named entity 'Square' named entity 'SARS-CoV-2' named entity 'machine learning' named entity 'Most' named entity 'SARS-CoV-2' named entity 'ARIMA' named entity 'machine learning' named entity 'ARIMA' named entity 'Confirmed Cases' named entity 'SARS-CoV-2' named entity 'ARIMA' named entity 'SARS-CoV-2' named entity 'medical staff' named entity '2.1' named entity 'COVID' named entity 'pattern recognition' named entity 'COVID' named entity 'GDP' named entity 'SVR' named entity 'coefficient of determination' named entity 'MSE' named entity 'ARIMA' named entity 'autoregressive integrated moving average' named entity 'PACF' named entity 'testing data set' named entity 'SVM' named entity 'ARIMA' named entity 'time series' named entity 'ARIMA' named entity '2019-nCoV' named entity 'COVID' named entity 'support vector machine' named entity 'overtraining' named entity 'equality type' named entity 'virus transmission' named entity 'non-linear regression' named entity 'COVID-19' named entity 'ARIMA' named entity 'WHO' named entity 'RMSE' named entity 'ARIMA' named entity 'vector' named entity 'coronavirus' named entity 'linear system of equations' named entity 'doubling time' named entity 'COVID-19' named entity 'virus' named entity 'COVID-19' named entity 'linear function' named entity 'Autoregressive Integrated Moving Average' named entity 'Time series' named entity 'COVID-19' named entity 'autocorrelation function' named entity 'COVID' named entity 'autocorrelation' named entity 'COVID' named entity 'Lohani' named entity 'Spain' named entity 'support vector machine' named entity 'virus' named entity 'highly contagious' named entity 'optimization problem' named entity 'contact tracing' named entity 'Maximum likelihood' named entity 'Parmar' named entity 'coronavirus disease' named entity 'ARIMA' named entity 'SVM' named entity 'France' named entity 'Lagrange multipliers' named entity 'support vector machine' named entity 'Ljung-Box test'

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