Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques.
Heliyon
; 9(2): e13483, 2023 Feb.
Article
in English
| MEDLINE | ID: covidwho-2263886
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Heliyon
Year:
2023
Document Type:
Article
Affiliation country:
J.heliyon.2023.e13483
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