A Comparison: Prediction of Death and Infected COVID-19 Cases in Indonesia Using Time Series Smoothing and LSTM Neural Network.
Procedia Comput Sci
; 179: 982-988, 2021.
Article
in English
| MEDLINE | ID: covidwho-1108629
ABSTRACT
COVID-19 is a virus causing pneumonia, also known as Corona Virus Disease. The first outbreak was found in Wuhan, China, in the province of Hubei on December 2019. The objective of this paper is to predict the death and infected COVID-19 in Indonesia using Savitzky Golay Smoothing and Long Short Term Memory Neural Network model (LSTM-NN). The dataset is obtained from Humanitarian Data Exchange (HDX), containing daily information on death and infected due to COVID-19. In Indonesia, the total data collected ranges from 2 March 2020 and by 26 July 2020, with a total of 147 records. The results of these two models are compared to determine the best fitted model. The curve of LSTM-NN shows an increase in death and infected cases and the Time Series also increases, however the smoothing shows a tendency to decrease. In conclusion, LSTM-NN prediction produce better result than the Savitzky Golay Smoothing. The LSTM-NN prediction shows a distinct rise and align with the actual Time Series data.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Procedia Comput Sci
Year:
2021
Document Type:
Article
Affiliation country:
J.procs.2021.01.102
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