Context: Signals recorded as multivariate time series by UV-Vis absorbance captors installed in urban sewer systems, can be non-stationary, yielding complications in the analysis of water quality monitoring. This work proposes to perform spectral estimation using the Box-Cox transformation and differentiation in order to obtain stationary multivariate time series in a wide sense. Additionally, Principal Component Analysis (PCA) is applied to reduce their dimensionality.Method: Three different UV-Vis absorbance time series for different Colombian locations were studied: (i) El-Salitre Wastewater Treatment Plant (WWTP) in Bogotá; (ii) Gibraltar Pumping Station (GPS) in Bogotá; and (iii) San-Fernando WWTP in Itagüí. Each UV-Vis absorbance time series had equal sample number (5705). The esti-mation of the spectral power density is obtained using the average of modified periodograms with rectangular window and an overlap of 50%, with the 20 most important harmonics from the Discrete Fourier Transform (DFT) and Inverse Fast Fourier Transform (IFFT).Results: Absorbance time series dimensionality reduction using PCA, resulted in 6, 8 and 7 principal components for each study site respectively, altogether explaining more than 97% of their variability. Values of differences below 30% for the UV range were obtained for the three study sites, while for the visible range the maximum differences obtained were: (i) 35% for El-Salitre WWTP; (ii) 61% for GPS; and (iii) 75% for San-Fernando WWTP.Conclusions: The Box-Cox transformation and the differentiation process applied to the UV-Vis absorbance time series for the study sites (El-Salitre, GPS and San-Fernando), allowed to reduce variance and to eliminate ten-dency of the time series. A pre-processing of UV-Vis absorbance time series is recommended to detect and remove outliers and then apply the proposed process for spectral estimation.Language: Spanish.