Enhanced Morphological Filtering for Wavelet-Based Changepoint Detection
Mattia Stasolla, Xavier Neyt · 2019
This paper presents a new method for the detection of abrupt changes (i.e. mean shifts) in time series. It is a follow-up to a previous article by the authors where, for the first time, the possibility of combining the multi-scale analysis capabilities of wavelets with mathematical morphology, a theoretical framework for the analysis of spatial structures, had been explored. The processing chain has been revised and enhanced in order to improve the overall results, and a performance assessment has been carried out to evaluate the accuracy and robustness of the method to noise, also providing a comparison with its original implementation.