SkyGraph : phase-based graph neural network for multivariate time series regression and classification

Kouridakis, Andreas; Κουριδάκης, Ανδρέας

dc.contributor.advisor	Vouros, George
dc.contributor.advisor	Βούρος, Γεώργιος
dc.contributor.author	Kouridakis, Andreas
dc.contributor.author	Κουριδάκης, Ανδρέας
dc.date.accessioned	2026-03-18T09:47:17Z
dc.date.available	2026-03-18T09:47:17Z
dc.date.issued	2026-03
dc.identifier.uri	https://dione.lib.unipi.gr/xmlui/handle/unipi/19024
dc.description	Not available until 31/03/2027
dc.format.extent	34	el
dc.language.iso	en	el
dc.publisher	Πανεπιστήμιο Πειραιώς	el
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/3.0/gr/	*
dc.title	SkyGraph : phase-based graph neural network for multivariate time series regression and classification	el
dc.type	Master Thesis	el
dc.contributor.department	Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων	el
dc.description.abstractEN	In recent years, Graph Neural Networks (GNNs) have been leveraged for multivariate time series (MTS) analysis, as they effectively model inter-variable relationships, addressing a key limitation of deep learning methods. However, most existing GNN approaches for MTS classification construct a single static graph over the entire time series, neglecting that inter-variable dependencies vary over time. To address these limitations, we propose SkyGraph, a GNN-based framework that captures inter-variable dependencies across different temporal segments through phase partitioning and attention-based message passing. In addition, we employ masked mean pooling across time dimension to obtain robust phase-level representations, for MTS of varying lengths. These representations are then integrated through a transformer encoder to produce the final sample representation, suitable for both regression and classification tasks. The proposed framework is evaluated in the context of estimating hidden flight parameters, specifically the cost index (CI) and payload mass (PL). These parameters are essential inputs for Air Traffic Management (ATM), yet airlines keep them confidential as business-sensitive data. Our results show a clear reduction in mean absolute error compared to related approaches in regression tasks, and high accuracy in classification tasks. To assess the generalization capability of our architecture across domains, we further evaluate it on three diverse datasets from the UEA archive, achieving competitive performance against state-of-the-art methods. An ablation study conducted across both task types confirms that all architectural components contribute significantly to the model's performance.	el
dc.corporate.name	National Center of Scientific Research "Demokritos"	el
dc.contributor.master	Τεχνητή Νοημοσύνη - Artificial Intelligence	el
dc.subject.keyword	Graph neural networks	el
dc.subject.keyword	Phase-based graph construction	el
dc.subject.keyword	Multivariate time series classification	el
dc.subject.keyword	Multivariate time series regression	el
dc.subject.keyword	Flight trajectories	el
dc.date.defense	2026-03-13

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Τμήμα Ψηφιακών Συστημάτων
Department of Digital Systems

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Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 3.0 Ελλάδα

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