tetano
Editor, Senior Moderator
Heliyon
. 2023 Feb;9(2):e13483.
doi: 10.1016/j.heliyon.2023.e13483. Epub 2023 Feb 3.
Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques
Saratu Yusuf Ilu[SUP] 1 [/SUP], Rajesh Prasad[SUP] 1 [/SUP]
Affiliations
Abstract
Purpose: The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task.
Methods: This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms.
Results: The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%).
Conclusions: The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.
Keywords: ARIMA; And clustering; Coronavirus; Feature selection; Machine learning; Prediction.
. 2023 Feb;9(2):e13483.
doi: 10.1016/j.heliyon.2023.e13483. Epub 2023 Feb 3.
Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques
Saratu Yusuf Ilu[SUP] 1 [/SUP], Rajesh Prasad[SUP] 1 [/SUP]
Affiliations
- PMID: 36776910
- PMCID: PMC9896886
- DOI: 10.1016/j.heliyon.2023.e13483
Abstract
Purpose: The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task.
Methods: This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms.
Results: The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%).
Conclusions: The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.
Keywords: ARIMA; And clustering; Coronavirus; Feature selection; Machine learning; Prediction.