Machine learning methods for modelling and analysis of time series signals in geoinformatics

Kaselimi, Maria; Κασελίμη, Μαρία

dc.contributor.author	Kaselimi, Maria	el
dc.contributor.author	Κασελίμη, Μαρία	en
dc.date.accessioned	2022-02-16T15:54:58Z
dc.date.available	2022-02-16T15:54:58Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54734
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.22432
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Deep learning	en
dc.subject	Long Short-term Memory	en
dc.subject	Convolutional Neural Networks	en
dc.subject	Total Electron Content (TEC)	en
dc.subject	Energy Disaggregation	en
dc.subject	Μηχανική Μάθηση	el
dc.subject	Χρονοσειρές	el
dc.subject	Μηχανική Μάθηση	el
dc.subject	Ίονοσφαιρική δραστηριότητα	el
dc.subject	Ενεργειακός Επιμερισμός	el
dc.subject	Αναδρομικά νευρωνικά δίκτυα	el
dc.title	Machine learning methods for modelling and analysis of time series signals in geoinformatics	en
heal.type	doctoralThesis
heal.secondaryTitle	Τεχνικές μηχανικής μάθησης για τη μοντελοποίηση και ανάλυση χρονοσειρών σε εφαρμογές γεωπληροφορικής	el
heal.classification	Geoinformatics	en
heal.classification	Deep Learning	en
heal.classification	Satellite Geodesy	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2021-09-06
heal.abstract	The analysis of experimental data that have been observed at different points in time leads to new and unique problems in statistical modeling and inference. The obvious correlation introduced by the sampling of adjacent points in time can severely restrict the applicability of the many conventional statistical methods traditionally dependent on the assumption that these adjacent observations are independent and identically distributed. The systematic approach by which one goes about answering the mathematical and statistical questions posed by these time correlations is commonly referred to as time series analysis (TSA). Time series modeling (TSM) plays a key role in a wide range of real-life problems that have a temporal component. Modern time series problems often pose significant challenges for the existing techniques both in terms of their complexity, structure and size. While traditional methods have focused on parametric models informed by domain expertise, modern machine learning (ML) methods provide a means to learn temporal dynamics in a purely data-driven manner. With the increasing data availability and computing power in recent times, machine learning has become a vital part of the next generation of time series models. Thus, there is both a great need and an exciting opportunity for the machine learning community to develop theory, models and algorithms specifically for the purpose of processing and analyzing time series data. The impact of time series modelling and analysis on scientific applications can be partially documented by analysing problems of various diverse fields in which important time series problems may arise. Modern time series problems are characterized by complexity. Also, since real-world systems often evolve under transient conditions, the signals/time series tend to exhibit various forms of non-stationarity. As far as mathematical models are concerned, they can be categorized in many different ways. They can be linear or non-linear, static or dynamic, continuous distinct in time, deterministic or contemplative. The proper model selection to accurately describe a system depends on the system under study, on whether the operation of the system is a-priory known or not, as well as on the purpose of the implementation. This dissertation presents developments in nonlinear and non-static time series models under a machine learning framework, comparing their performance in real-life application scenarios related to geoinformatics as well as environmental applications. 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 Geoinformatics communities. 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 Non-intrusive 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.	en
heal.sponsor	Το έργο συγχρηματοδοτείται από την Ελλάδα και την Ευρωπαϊκή Ένωση (Ευρωπαϊκό Κοινωνικό Ταμείο) μέσω του Επιχειρησιακού Προγράμματος «Ανάπτυξη Ανθρώπινου Δυναμικού, Εκπαίδευση και Διά Βίου Μάθηση», στο πλαίσιο της Πράξης «Ενίσχυση του ανθρώπινου ερευνητικού δυναμικού μέσω της υλοποίησης διδακτορικής έρευνας» (MIS-5000432), που υλοποιεί το Ίδρυμα Κρατικών Υποτροφιών (ΙΚΥ)	el
heal.advisorName	Doulamis, Nikolaos
heal.advisorName	Δουλάμης, Νικόλαος	el
heal.committeeMemberName	Δεληκαράογλου, Δημήτριος	el
heal.committeeMemberName	Σταφυλοπάτης, Ανδρέας Γεώργιος	el
heal.committeeMemberName	Βουλόδημος, Αθανάσιος	el
heal.committeeMemberName	Βαρβαρίγος, Εμμανουήλ	el
heal.committeeMemberName	Γκίκας, Βασίλειος	el
heal.committeeMemberName	Τσακίρη, Μαρία	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Αγρονόμων και Τοπογράφων Μηχανικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	160 σ.	el
heal.fullTextAvailability	false