In this article a methodology for filtering a time series is presented, with application to high frequency series such as the minute-by-minute electric load series. The goal of this approach is to detect and substitute the irregularities of the time series that can produce distortions on the modelling stage. Outlier values are detected through a dynamic linear model and the Bayes factor tool; missing values are then interpolated with a Smoothing Cubic Spline. The performance of the proposed approach is illustrated using real data and evaluated through a series of tests where the irregularities have been simulated.Keywords: load series; DLM model; irregularities in time series; cubic spline; Bayes factor. ResumoNeste artigo apresenta-se uma metodologia para a filtragem de séries temporais, com aplicação em séries de alta freqüência. Esta metodologia tem como objetivo detectar e substituir as irregularidades da série temporal que podem comprometer a etapa de modelagem. São detalhados o modelo linear dinâmico utilizado para detectar os valores outliers e o emprego do Fator de Bayes. Na interpolação de valores faltantes utiliza-se o Spline Cúbico Suavizado. O desempenho da metodologia proposta é avaliado a través de vários testes onde as irregularidade foram simuladas.Palavras-chave: série de cargas; modelo linear dinâmico; irregularidades em séries temporais; spline cúbico; fator de Bayes.
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