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

Leila Ismail; Shaikhah Alhmoudi; Sumyah Alkatheri

doi:10.1109/CSCI51800.2020.00150

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

Leila Ismail, Shaikhah Alhmoudi, Sumyah Alkatheri

Department of Computer Science and Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (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 language	English
Title of host publication	Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	801-806
Number of pages	6
ISBN (Electronic)	9781728176246
DOIs	https://doi.org/10.1109/CSCI51800.2020.00150
Publication status	Published - Dec 2020
Event	2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, United States Duration: Dec 16 2020 → Dec 18 2020

Publication series

Name	Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conference

Conference	2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Country/Territory	United States
City	Las Vegas
Period	12/16/20 → 12/18/20

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

Access to Document

10.1109/CSCI51800.2020.00150

Cite this

Ismail, L., Alhmoudi, S., & Alkatheri, S. (2020). Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA. In Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 (pp. 801-806). [9457853] (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI51800.2020.00150

Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA. / Ismail, Leila; Alhmoudi, Shaikhah; Alkatheri, Sumyah.
Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 801-806 9457853 (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ismail, L, Alhmoudi, S & Alkatheri, S 2020, Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA. in Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020., 9457853, Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 801-806, 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Las Vegas, United States, 12/16/20. https://doi.org/10.1109/CSCI51800.2020.00150

Ismail L, Alhmoudi S, Alkatheri S. Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA. In Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 801-806. 9457853. (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020). doi: 10.1109/CSCI51800.2020.00150

Ismail, Leila ; Alhmoudi, Shaikhah ; Alkatheri, Sumyah. / Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA. Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 801-806 (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020).

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title = "Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA",

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. ",

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author = "Leila Ismail and Shaikhah Alhmoudi and Sumyah Alkatheri",

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

Abstract

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Keywords

ASJC Scopus subject areas

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