Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting

Sundus Naji Alaziz, Bakr Albayati, Abd al-Aziz H. El-Bagoury and Wasswa Shafik
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Sundus Naji Alaziz: Department of Mathematical Sciences, Faculty of Science, Princess Nourah bint Abdulrahman University, Saudi Arabia
Bakr Albayati: Department of Basic Sciences, Common First Year King Saud University, Riyadh, Saudi Arabia
Abd al-Aziz H. El-Bagoury: Basic Sciences Department, Higher Institute of Engineering and Technology, El-Mahala El-Kobra, Egypt

International Journal of Data Warehousing and Mining (IJDWM), 2023, vol. 19, issue 3, 1-25

Abstract: The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. Based on this proposed method, the authors address the conclusions.

Date: 2023
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