  In this dissertation is provided a comparative analysis that evaluates the
performance of several deep learning (DL) architectures on a large number of
time series datasets of different nature and for different applications. Two
main fruitful research fields are discussed here which were strategically
chosen in order to address current cross disciplinary research priorities
attracting the interest of geodetic community. The first problem is related to
ionospheric Total Electron Content (TEC) modeling which is an important issue
in many real time Global Navigation System Satellites (GNSS) applications.
Reliable and fast knowledge about ionospheric variations becomes increasingly
important. GNSS users of single frequency receivers and satellite navigation
systems need accurate corrections to remove signal degradation effects caused
by the ionosphere. Ionospheric modeling using signal processing techniques is
the subject of discussion in the present contribution. The next problem under
discussion is energy disaggregation which is an important issue for energy
efficiency and energy consumption awareness. Reliable and fast knowledge about
residential energy consumption at appliance level becomes increasingly
important nowadays and it is an important mitigation measure to prevent energy
wastage. Energy disaggregation or Nonintrusive load monitoring (NILM) is a
single channel blind source separation problem where the task is to estimate
the consumption of each electrical appliance given the total energy
consumption. For both problems various deep learning models (DL) are proposed
that cover various aspects of the problem under study, whereas experimental
results indicate the proposed methods superiority compared to the current state
of the art.
