Feature-preserving interpolation and filtering of environmental time series

TL;DR

This paper introduces a non-parametric, pattern-matching method for filling gaps and removing interferences in environmental time series, effectively preserving spectral features and variable dependencies across multiple applications.

Contribution

It presents a novel iterative pattern-matching approach that jointly considers multiple variables and quantifies uncertainty through multiple realizations.

Findings

Successfully reproduces spectral features of original signals

Significantly improves data quality even with intense perturbations

Corrects bias caused by asymmetrical interferences

Abstract

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.