Análise fatorial em series temporais com long-memory, outliers e sazonalidade : aplicação em poluição do ar na região da Grande Vitória-ES

• Ano de defesa: 2015
• Autor(a) principal: Sgrancio, Adriano Marcio
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Doutorado em Engenharia Ambiental; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Ambiental
• Palavras-chave em Português: factor analysis; air pollution; time series analysis; long memory; MP10; SO2; Outliers; Robustez; Análise fatorial; Ar – Poluição; Análise de séries temporais; Valores estranhos (Estatística); Estatística robusta; Dióxido de enxofre; Material particulado; Poluição do ar; Longa dependência; Engenharia sanitária; 628
• Link de acesso: http://repositorio.ufes.br/handle/10/1654

Studies about air pollution typically involve measurements and analysis of pollutants, such as PM10 (particulate matter), SO2 (sulfur dioxide) and others. These data typically have important features like serial correlation, long dependency, seasonality and occurence of atypical observations, and many others, which may be analyzed by means of multivariate time series. In this context, a robust estimator of fractional robust autocovariance matrix of long dependence and seasonal frequency for SARFIMA model is proposed. The model is compared to SARMA model and is applied to SO2 concentrations. In addition of the mentioned features the data present high dimensionality in relation to sample size and number of variables. This fact complicates the analisys of the data using vector time series models. In the literature, the approach to mitigate this problem for high dimensional time series is to reduce the dimensionality using the factor analysis and principal component analysis. However, the long dependence characteristics and atypical observations, very common in air pollution series, is not considered by the standard factor analysis method. In this context, the standard factor model is extended to consider time series data presenting long dependence and outliers. The proposed method is applied to PM10 series of air quality monitoring network of the Greater Vitoria Region - ES.