Alimohammadi Ardekani, Majid, Karimi-Zarchi, Mohammad Hossein, Shishebori, Davood. (1403). Analyzing and Forecasting of Coronavirus Time-Series Data: Performance Comparison of Machine Learning and Statistical Models. , 58(2), 291-306. doi: 10.22059/aie.2024.380927.1906
Majid Alimohammadi Ardekani; Mohammad Hossein Karimi-Zarchi; Davood Shishebori. "Analyzing and Forecasting of Coronavirus Time-Series Data: Performance Comparison of Machine Learning and Statistical Models". , 58, 2, 1403, 291-306. doi: 10.22059/aie.2024.380927.1906
Alimohammadi Ardekani, Majid, Karimi-Zarchi, Mohammad Hossein, Shishebori, Davood. (1403). 'Analyzing and Forecasting of Coronavirus Time-Series Data: Performance Comparison of Machine Learning and Statistical Models', , 58(2), pp. 291-306. doi: 10.22059/aie.2024.380927.1906
Alimohammadi Ardekani, Majid, Karimi-Zarchi, Mohammad Hossein, Shishebori, Davood. Analyzing and Forecasting of Coronavirus Time-Series Data: Performance Comparison of Machine Learning and Statistical Models. , 1403; 58(2): 291-306. doi: 10.22059/aie.2024.380927.1906

Analyzing and Forecasting of Coronavirus Time-Series Data: Performance Comparison of Machine Learning and Statistical Models

Advances in Industrial Engineering
دوره 58، شماره 2، اسفند 2024، صفحه 291-306    اصل مقاله (599.23 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/aie.2024.380927.1906
نویسندگان
Majid Alimohammadi Ardekani1؛ Mohammad Hossein Karimi-Zarchi2؛ Davood Shishebori* 3
1Assistant Professor, Department of industrial engineering, faculty of engineering, Ardakan University, Ardakan, Iran.
2Ph.D. Candidate, Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran.
3Associate Professor, Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran.
چکیده
Coronavirus is a respiratory disease caused by coronavirus 2 acute respiratory syndrome. Forecasting the number of new cases and deaths can be an efficient step towards predicting costs and providing timely and sufficient facilities needed in the future. The goal of the current study is to accurately formulate and predict new cases and mortality in the future. Nine prediction models are tested on the Coronavirus data of Yazd province as a case study. Due to the evaluation criteria of root mean square error (RMSE), mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute value of error (MAE), the models are compared. The analysis results emphasize that, according to the mentioned evaluation criteria, the KNN regression model and the BATS model are the best models for predicting the cumulative cases of hospitalization of Coronavirus and the cumulative cases of death, respectively. Moreover, for both hospitalization and death cases, the autoregressive neural network model has the worst performance among other formulations.
کلیدواژه‌ها
Pandemic؛ Coronavirus؛ Time Series؛ Forecasting؛ Statistical Modeling؛ Machine Learning Models
مراجع
آمار
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