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Identifier |
000430858 |
Title |
Data-driven symbolic representations for high-level time series analysis |
Alternative Title |
Συμβολικές αναπαραστάσεις βάσει δεδομένων για ανάλυση χρονοσειρών σε υψηλό επίπεδο |
Author
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Μπουντρογιάννης, Κωνσταντίνος Ε.
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Thesis advisor
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Τσακαλίδης, Παναγιώτης
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Reviewer
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Τζαγκαράκης, Γεώργος
Καρυστινός, Γεώργιος
Τζίτζικας, Ιωάννης
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Abstract |
The systematic collection of data has become an intrinsic process of all aspects in modern life. From industrial to healthcare machines and wearable sensors, an unprecedented amount of data is becoming available for mining and information retrieval. The ever-increasing volume and complexity of time series data necessitate efficient dimensionality reduction for facilitating data mining tasks. Symbolic representations, especially the family of symbolic aggregate approximations (SAX), have proven very effective in compacting the information content of time series while exploiting the wealth of search algorithms used in bioinformatics and text mining communities. However, typical SAX-based techniques rely on a Gaussian assumption for the underlying data statistics, which often deteriorates their performance in practical scenarios. To overcome this limitation, this thesis introduces a method that negates any assumption on the probability distribution of time series, by means of kernel density estimation (KDE) and Lloyd-Max quantization. Experimental evaluation on real-world datasets demonstrates the superiority of the proposed method, when compared against the conventional SAX and an alternative data-adaptive SAX-based method. Finally, in the present thesis, the proposed dimensionality reduction method is utilized to provide compact representations of time series for the purposes of anomaly detection. To this end, a computationally efficient, yet highly accurate, framework for anomaly detection of streaming data in lower-dimensional spaces is developed, whereas alternative quantization schemes are explored and utilized for more accurate statistical inference.
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Language |
English |
Subject |
Data-driven probabilistic SAX |
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Kernel density estimation |
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LLOYD-MAX quantization |
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Mode-bounding quantizer |
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Online anomaly detection |
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Symbolic representations |
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Ανίχνευση ανωμαλιών σε πραγματικό χρόνο |
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Εκτίμηση πυκνότητας πιθανότητας |
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Κβάντιση LLOYD-MAX |
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Κβάντιση σε MODES |
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Πιθανοτική SAX |
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Συμβολικές αναπαραστάσεις |
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Χρονοσειρές |
Issue date |
2020-07-24 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/0/6/2/metadata-dlib-1595490418-631721-816.tkl
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Views |
532 |