Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA

Leila Ismail, Shaikhah Alhmoudi, Sumyah Alkatheri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Machine learning time series models have been used to predict COVID-19 pandemic infections. Based on the public dataset from Johns Hopkins, we present a novel framework for forecasting COVID-19 infections. We implement our framework for the United Arab Emirates (UAE) and develop autoregressive integrated moving average (ARIMA) time series forecast model. To the best of our knowledge, this is the only study to forecast the infections in UAE using the time series model.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages801-806
Number of pages6
ISBN (Electronic)9781728176246
DOIs
Publication statusPublished - Dec 2020
Event2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, United States
Duration: Dec 16 2020Dec 18 2020

Publication series

NameProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conference

Conference2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Country/TerritoryUnited States
CityLas Vegas
Period12/16/2012/18/20

Keywords

  • COVID-19
  • autoregressive integrated moving average (ARIMA)
  • coronavirus
  • machine learning
  • time series

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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