A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA
Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2223141
ABSTRACT
Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022
Year:
2022
Document Type:
Article
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