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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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Full text: Available 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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Full text: Available 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