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How to cite (IEEE):
A. Ahmar, & E. Boj
"Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil," JOIV : International Journal on Informatics Visualization, vol. 5, no. 1, , pp. 8-10, Mar. 2021.
https://doi.org/10.30630/joiv.5.1.372
How to cite (APA):
Ahmar, A., & Boj, E.
(2021).
Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil.
JOIV : International Journal on Informatics Visualization, 5(1), 8-10.
https://doi.org/10.30630/joiv.5.1.372
How to cite (Chicago):
Ahmar, Ansari Saleh, AND Boj, Eva.
"Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil" JOIV : International Journal on Informatics Visualization [Online], Volume 5 Number 1 (18 March 2021)
https://doi.org/10.30630/joiv.5.1.372
How to cite (Vancouver):
Ahmar A, & Boj E .
Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil.
JOIV : International Journal on Informatics Visualization [Online]. 2021 Mar;
5(1):8-10.
https://doi.org/10.30630/joiv.5.1.372
How to cite (Harvard):
Ahmar, A., & Boj, E.
,2021.
Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil.
JOIV : International Journal on Informatics Visualization, [Online] 5(1), pp. 8-10.
https://doi.org/10.30630/joiv.5.1.372
How to cite (MLA8):
Ahmar, Ansari Saleh, & Eva Boj.
"Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil." JOIV : International Journal on Informatics Visualization [Online], 5.1 (2021): 8-10. Web. 2 Dec. 2023
, https://doi.org/10.30630/joiv.5.1.372
BibTex Citation Data :
@article{JOIV372,
author = {Ansari Saleh Ahmar and Eva Boj},
title = {Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {5},
number = {1},
year = {2021},
keywords = {Infection disease; COVID-19; forecasting; NNAR; ARIMA.},
abstract = {Forecasting is a method that is often used to view future events using past time data. Past time data have useful information to use in obtaining the future. The aim of this study was to forecast infection fatality rate (IFR) of COVID-19 in Brazil using NNAR and ARIMA. ARIMA and NNAR are used because (1) ARIMA is a simple stochastic time series method that can be used to train and predict future time points and ARIMA also capable of capturing dynamic interactions when it uses error terms and observations of lagged terms; (2) the Artificial Neural Network (ANN) is a technique capable of analyzing certain non-linear interactions between input regressor and responses, and Neural Network Time Series (NNAR) is one method of ANN in which lagged time series values were used as inputs to a neural network. Data included in this study were derived from the total data of confirmed cases and the total data of death of COVID-19. The data of COVID-19 in Brazil from February 15, 2020 to April 30, 2020 were collected from the Worldometer (https://www.worldometers.info/coronavirus/) and Microsoft Excel 2013 was used to build a time-series table. Forecasting was accomplished by means of a time series package (forecast package) in R Software. Neural Network Time Series and ARIMA models were applied to a dataset consisting of 76 days. The accuracy of forecasting was examined by means of an MSE. The forecast of IFR of COVID-19 in Brazil from May 01, 2020 to May 10, 2020 with NNAR (1,1) model was around in 6,85% and ARIMA (0,2,1) was around in 7.11%.},
issn = {2549-9904}, pages = {8--10}, doi = {10.30630/joiv.5.1.372},
url = {http://joiv.org/index.php/joiv/article/view/372}
}
Refworks Citation Data :
@article{{JOIV}{372}, author = {Ahmar, A., Boj, E.}, title = {Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {5}, number = {1}, year = {2021}, doi = {10.30630/joiv.5.1.372}, url = {} }Refbacks
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JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.