Change-point detection in environmental time series based on the informational approach


In this study, the Schwarz Information Criterion (SIC) is applied in order to detect change-points in the time series of surface water quality variables. The application of change-point analysis allowed detecting change-points in both the mean and the variance in series under study. Time variations in environmental data are complex and they can hinder the identification of the so-called change-points when traditional models are applied to this type of problems. The data seasonality structure is incorporated through a linear modeling approach. The assumptions of normality and uncorrelation are not present in some time series, and so, a simulation study is carried out in order to evaluate the methodology’s performance when applied to non-normal data and/or with time correlation.

DOI Code: 10.1285/i20705948v9n2p267

Keywords: Change-point detection;Water quality data;Schwarz Information Criterion;Mean and variance shift;Simulation study


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