基于高斯过程和最小中位数平方线性回归的西爪哇地区新型冠状病毒COVID-19数据时间序列预测

IF 1.4 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
I. Yulita, Firman Ardiansyah, Aulia Siska, I. Suryana
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引用次数: 0

摘要

2019年,新冠肺炎疫情席卷全球。该病毒最初是在中国武汉发现的。几个月过去了,这种病毒已经传播到世界各地的许多地方。因此,这种病毒已成为世界性的大流行病。为限制这种病毒的传播,已作出多项努力。一个可能的行动方针是封锁领土。不幸的是,这一策略破坏了经济,使糟糕的形势进一步恶化。如果没有新的病例,世界卫生组织(世卫组织)将松一口气。但是,除了现有的数据之外,政府应该探索利用未来的数据。时间序列的预测可用于此目的。研究表明,高斯过程方法优于最小中位数平方线性回归方法(LMSLR)。应用基于Pearson vii的全局核得到的MAE和RMSE值分别为23.12和53.43。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression
In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.
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来源期刊
Decision Science Letters
Decision Science Letters Decision Sciences-Decision Sciences (all)
CiteScore
3.40
自引率
5.30%
发文量
49
审稿时长
20 weeks
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