E3S Web Conf.
Volume 202, 2020The 5th International Conference on Energy, Environmental and Information System (ICENIS 2020)
|Number of page(s)||9|
|Section||Industrial and Health Information System|
|Published online||10 November 2020|
ARFIMA Model for Short Term Forecasting of New Death Cases COVID-19
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang – Indonesia
* Corresponding author: email@example.com
COVID-19 is an infectious disease that can spread from one person to another and has a high potential for death. The infection of COVID-19 is spreading massive and fast that causes the extreme fluctuating data spread and long memory effects. One of the ways in which the death of COVID-19 can be reduce is to produce a prediction model that could be used as a reference in taking countermeasures. There are various prediction models, from regression to Autoregressive Fractional Integrated Moving Average (ARIMA), but it still shows shortcomings when disturbances occur from extreme fluctuations and the existence of long memory effects in the form of analysis of a series of data becomes biased, and the power of statistical tests generated for identification become weak. Therefore, the prediction model with the Autoregressive Fractional Integrated Moving Average (ARFIMA) approach was used in this study to accommodate these weaknesses because of their flexible nature and high accuracy. The results of this study prove that ARFIMA (1,0,431.0) with an RMSE of 2,853 is the best model to predict data on the addition of new cases of patients dying from COVID-19.
Key words: ARFIMA / Prediction / Death / COVID-19
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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