利用各种时间序列模型预测印度尼西亚的COVID-19

Media Statistika Pub Date : 2022-06-15 DOI:10.14710/medstat.15.1.83-93

G. Darmawan, D. Rosadi, B. N. Ruchjana, R. Pontoh, Asrirawan Asrirawan, W. Setialaksana

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引用次数: 0

摘要

在本研究中，使用时间序列模型对印度尼西亚的新冠肺炎进行建模。所使用的时间序列模型是离散数据的时间序列模式。这些模型包括前馈神经网络（FFNN）、误差、趋势和季节（ETS）、奇异谱分析（SSA）、模糊时间序列（FTS）、广义自回归移动平均（GARMA）和贝叶斯时间序列。基于使用MAPE（Mean Absolute Percentage Error，平均绝对百分比误差）作为已确认数据的模型评估的预测精度计算结果，最准确的病例模型是0.04%的贝叶斯模型，而除FTS=0.06%外，所有恢复病例的MAPE均为0.05%。对于死亡病例SSA和贝叶斯模型的数据，使用MAPE的最佳值为0.07%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS

In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.

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Media Statistika

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审稿时长

7 weeks