April 2025
Intermediate to advanced
324 pages
7h 7m
Portuguese (Portugal, Brazil)
Content preview from Pensa em Estatística, 3ª Edição
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Capítulo 12. Análise de séries temporais
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Uma série temporal é uma sequência de medições de um sistema que varia no tempo. Muitas das ferramentas que utilizámos nos capítulos anteriores, como a regressão, também podem ser utilizadas com séries cronológicas. Mas há métodos adicionais que são particularmente úteis para este tipo de dados.
Como exemplos, analisaremos dois conjuntos de dados: produção de eletricidade renovável nos Estados Unidos de 2001 a 2024 e dados meteorológicos no mesmo intervalo. Desenvolveremos métodos para decompor uma série temporal numa tendência a longo prazo e numa componente sazonal repetida. Utilizaremos modelos de regressão linear para ajustar e prever tendências. E vamos experimentar um modelo amplamente utilizado para analisar dados de séries temporais, com o nome formal de "média móvel integrada autoregressiva" e o acrónimo ARIMA, mais fácil de dizer.
Eletricidade
Como exemplo de dados de séries temporais, utilizaremos um conjunto de dados da Administração de Informação sobre Energia dos EUA - inclui a produção total de eletricidade por mês a partir de fontes renováveis de 2001 a 2024. As instruções para descarregar os dados encontram-se no bloco de notas deste capítulo.
Depois de carregar os dados, temos de fazer algumas transformações para os colocar num formato que seja fácil de trabalhar:
elec=(pd.read_csv("Net_generation_for_all_sectors.csv" ...
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