  We propose a method for filling gaps and removing interferences in time
series for applications involving continuous monitoring of environmental
variables. The approach is non-parametric and based on an iterative
pattern-matching between the affected and the valid parts of the time series.
It considers several variables jointly in the pattern matching process and
allows preserving linear or non-linear dependences between variables. The
uncertainty in the reconstructed time series is quantified through multiple
realizations. The method is tested on self-potential data that are affected by
strong interferences as well as data gaps, and the results show that our
approach allows reproducing the spectral features of the original signal. Even
in the presence of intense signal perturbations, it significantly improves the
signal and corrects bias introduced by asymmetrical interferences. Potential
applications are wide-ranging, including geophysics, meteorology and hydrology.
