Al-Turaiki Isra, Almutlaq Fahad, Alrasheed Hend, Alballa Norah
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.
Int J Environ Res Public Health. 2021 Aug 16;18(16):8660. doi: 10.3390/ijerph18168660.
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.
新冠病毒病是一种由冠状病毒毒株引起的疾病，该毒株于2019年12月出现，导致了一场持续的全球大流行。预测大流行走向的能力至关重要。这对于确定如何抗击和追踪其传播很重要。新冠病毒病数据是时间序列数据的一个例子，有几种方法可用于预测。虽然有各种时间序列预测模型，但很难就它们的相对优点得出广泛的理论结论。本文对几种用于预测沙特阿拉伯新冠病毒病病例、康复人数和死亡人数的时间序列模型进行了实证评估。具体而言，使用自回归积分滑动平均法、TBATS、指数平滑法、三次样条法、简单指数平滑霍尔特法和霍尔特-温特斯法训练了七种预测模型。这些模型是使用沙特阿拉伯报告的2020年3月24日至2021年4月5日期间公开可用的新冠病毒病每日数据构建的。实验结果表明，自回归积分滑动平均模型在预测确诊病例时预测误差较小，这与文献报道的结果一致，而三次样条法在预测康复人数和死亡人数方面表现更好。随着更多数据可用，观察到预测准确性指标出现波动，这可能是由于数据的突然变化所致。
