Detecção de outliers em séries espaço-temporais: análise de precipitação em Minas Gerais

• Autor(a) principal: Silva, Alyne Neves
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://locus.ufv.br/handle/123456789/4061
• topic: Inconsistências; ARIMA; Autovalidação; Outliers; Inconsistencies; ARIMA; self-validation; Outliers; CNPQ::CIENCIAS AGRARIAS

Time series are sometimes influenced by disruptions of events, such as strikes, the outbreak of war, among others. These interrupts originate atypical observations or outliers that directly influence the homogeneity of the series, leading to erroneous inferences and interpretations of the variable under study, being very common in climatological data. So, in the interest of detecting outliers in time series of precipitation, this study aimed to establish a method of detecting outliers. For this, there was the junction of ARIMA models and methodologies of the classical geostatistics, the self-validation. The proposed criterion compares waste of time series analysis with confidence intervals of the residue of self-validation. We analyzed time series of average monthly rainfall for rainy days of 43 rainfall stations in the state of Minas Gerais, between the years 2000 to 2005. The analysis procedures ranging from the description of the periodicity through the periodogram to obtain validation, from the estimation of the semivariogram models by ordinary least squares methods and maximum likelihood. The results for the period under study, 165 were detected outliers, spread between the 43 rainfall stations. The station Campo Grande Ranch, located in the municipality of Passa Tempo, was the season in which they recorded the highest number of outliers, 45 in total. As the results, we considered the proposed method very efficient in detecting outliers, and therefore the analysis of the homogeneity of observations.