Impact of the COVID-19 pandemic on IBOVESPA: A statistical analysis with machine learning models PROPHET and AUTOARIMA

被引：0

作者：

Silva, Antonio Victor Alves ^{[1
]}

Xavier, Erika Fialho Morais ^{[2
]}

Barbosa, Nyedja Fialho Morais ^{[1
]}

Xavier Junior, Silvio Fernando Alves ^{[1
]}

Jale, Jader da Silva ^{[3
]}

机构：

[1] Univ Estadual Paraiba UEPB, Campina Grande, Brazil

[2] Fiocruz MS, Ctr Integracao Dados & Conhecimentos Saude Cidacs, Rio De Janeiro, Brazil

[3] Univ Fed Rural Pernambuco, Dept Estat & Informat DEINFO, Recife, Brazil

来源：

SIGMAE | 2024年 / 13卷 / 02期

关键词：

Statistical modeling; Time series; Forecasting;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This work analyzed the impact of the covid-19 pandemic in the year 2020 on Brazilian stocks using the Bovespa index and identified outliers in the data. It also observed a trend of stability in the following years, indicating economic recovery. The seasonality in the regular patterns was identified and represented in a line graph, highlighting the lowest medians in June and July. Prophet and autoARIMA models were used for forecasting, and the results were evaluated using various error metrics, including differentiated data, the AutoARIMA model performed better with the original and log1p transformed data. The study is relevant to understand the impact of the pandemic on Brazilian stocks and how forecasting models can be used to assist in decision-making.

引用

页码：57 / 71

页数：15

共 19 条

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