About: Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
Creator	Chatterjee, Saikat Javid, Alireza Liang, Xinyue Venkitaraman, Arun
source	ArXiv
abstract	We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main objective is to provide predictions of the number of infected people for different countries in the next 14 days. The predictive analysis is done using time-series data transformed on a logarithmic scale. We use two well-known methods for prediction: polynomial regression and neural network. As the number of training data for each country is limited, we use a single-layer neural network called the extreme learning machine (ELM) to avoid over-fitting. Due to the non-stationary nature of the time-series, a sliding window approach is used to provide a more accurate prediction.
has issue date	2020-05-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	6e8eeb2ef4c6855af75d9ed14c9f295b087523ad
resource representing a document's title	Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
resource representing a document's body	covid:6e8eeb2ef4c6855af75d9ed14c9f295b087523ad#body_text
is schema:about of	named entity 'provide' named entity 'training data' named entity 'provide' named entity 'nature' named entity 'Time-series' »more»

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