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Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

Author

Listed:
  • Isra Al-Turaiki

    (Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    First joint author.)

  • Fahad Almutlaq

    (Geography Department, College of Arts, King Saud University, Riyadh 11451, Saudi Arabia
    First joint author.)

  • Hend Alrasheed

    (Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Norah Alballa

    (Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

Suggested Citation

  • Isra Al-Turaiki & Fahad Almutlaq & Hend Alrasheed & Norah Alballa, 2021. "Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8660-:d:615589
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    References listed on IDEAS

    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. C. Chatfield, 1978. "The Holt‐Winters Forecasting Procedure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 264-279, November.
    3. Fuad A Awwad & Moataz A Mohamoud & Mohamed R Abonazel, 2021. "Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    4. Emrah Gecili & Assem Ziady & Rhonda D Szczesniak, 2021. "Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-11, January.
    5. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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    7. Hend Alrasheed & Alhanoof Althnian & Heba Kurdi & Heila Al-Mgren & Sulaiman Alharbi, 2020. "COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis," IJERPH, MDPI, vol. 17(21), pages 1-24, October.
    8. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
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