Forecasting Diurnal Covid-19 Cases for Top-5 Countries Using Various Time-series Forecasting Algorithms
4th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-1846086
ABSTRACT
On January 30, 2020, the World Health Organisation classified the Covid-19 outbreak a Public Health Emergency of International Concern, and a pandemic was proclaimed on March 11, 2020. Two years after the Covid-19 outbreak, the virus has new transmutations plus is turning out to be more difficult for forecasting in terms of both its behaviour and severity. Various techniques for time series analysis of coronavirus (Covid-19) cases were examined in this study. The Deep Learning model chosen, Long Short-Term Memory (LSTM) is compared against Statistical approaches, such as Linear Regression, Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), based on a variety of performance metrics. Following the estimates of the superior algorithm, medical care professionals can act at the appropriate moment to supply Equipment to health care institutions and further help the public. According to our data, as the number of projected days grows, so does the model's error rate. Forecasted trends also suggest that statistical approaches are relatively better overall for predictions of fewer days, but Deep Learning methods are relatively better for forecasts of more days. © 2022 IEEE.
Covid-19; Deep Learning; Statistical Modelling; Time-series forecast; Coronavirus; Health care; Long short-term memory; Time series analysis; Autoregressive integrated moving average(ARIMA); Classifieds; Forecasting algorithm; Statistic modeling; Statistical approach; Time series forecasting; Time series forecasts; World Health Organization; Forecasting
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
4th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2022
Year:
2022
Document Type:
Article
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