Clusterization and decomposition techniques for time series have been around for mor than 50 years, but is now that the computational power at disposition makes them an appealing strategy to deal with massive data sets comprehending million of entries. Subsidence, as natural and anthropogenic originated phenomenon, constitutes the perfect sample to put them in practice since it requires thousand and million of points in order to properly characterize an entire region in terms of ground vertical movement, and is, for this reason, we decided to address its analysis with a fresh and novel approach that, until the moment of publication of this thesis, hasn’t been adopted before.