Detection of outliers and replacement of missing values in absorbance and discharge time series

The present paper proposes a methodology aiming to detect and remove outliers as well as to fill gaps in time series. One notable application of such a methodology would be the comparison of dry weather contributions to those of rain events, after detection and removal of outliers. The proposed methodology includes outlier detection and application of the Discrete Fourier Transform (DFT). Together, these tools were used to analyse a case study including four time series: three UV-Vis spectra series from study sites in Colombia and one discharge series in France. Outlier detection with the proposed methodology gives good results when window parameter values are small and self-similar, despite the fact that the four time series exhibited different lengths and behaviours. The DFT allows completing time series based on its ability to manage various gap sizes, remove outliers and replace missing values. DFT led to low error percentages for all four time series (14 % in average). This percentage reflects what would have likely been the time series behaviour in the absence of misleading outliers and missing data.