Post-graduate theses
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Identifier |
000456945 |
Title |
Choose wisely: an extensive evaluation of model selection for anomaly detection in time series |
Alternative Title |
Επιλέξτε προσεκτικά: μια εκτενής αξιολόγηση επιλογής μοντέλων για την ανίχνευση ανωμαλιών σε χρονοσειρές |
Author
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Συλλιγάρδος, Εμμανουήλ Π.
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Thesis advisor
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Τραχανιάς, Πσναγιώτης
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Reviewer
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Κομοντάκης, Νικόλαος
Παλπάνας, Θέμης
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Abstract |
Anomaly detection is a fundamental task for time-series analysis with important implications for the downstream performance of many applications. Despite
increasing academic interest and the large number of methods proposed in the
literature, recent benchmark and evaluation studies demonstrated that no overall
best anomaly detection methods exist when applied to very heterogeneous time
series datasets. This lack of a universally superior method poses a significant
challenge for practitioners who need to select the most appropriate technique for
their specific datasets. To overcome this limitation, this thesis proposes a model
selection pipeline that can automatically determine the best anomaly detection
technique based on the characteristics of the time series data. By leveraging time
series classification algorithms for model selection, the goal is to provide a scalable and viable solution to solve anomaly detection over highly diverse time series
collected from various domains.
Existing AutoML solutions are not directly applicable to time series anomaly
detection, and no evaluation of time series-based approaches for model selection
currently exists. Accordingly, we compare 16 different classifiers over 1800 time
series, representing a diverse range of datasets. By comparing the performance
of these classifiers, the study provides the first comprehensive evaluation of time
series classification as a model selection approach for anomaly detection. The results demonstrate that model selection methods outperform individual anomaly
detection methods while maintaining execution times in the same order of magnitude. This evaluation serves as a crucial first step in demonstrating the accuracy
and efficiency of time series classification algorithms for anomaly detection, set-
ting a strong baseline that can guide the model selection step in general AutoML
pipelines.
The findings of this evaluation have significant implications for the field of
time series anomaly detection. The demonstrated superiority of model selection
methods over individual anomaly detection techniques highlights the importance
of selecting the most appropriate method based on time series characteristics. By
capitalizing on the strengths of different anomaly detection methods, practitioners
can enhance the overall performance of their anomaly detection systems. More-
over, the evaluation serves as a benchmark for comparing and selecting time series
classification algorithms for model selection purposes in anomaly detection tasks.
This comprehensive study not only provides valuable insights into the effectiveness of various classifiers, but also establishes a foundation for further research and
development in automated model selection approaches for time series anomaly detection. Ultimately, the proposed model selection method and the experimental
evaluation contribute to advancing the state-of-the-art in time series analysis and
enable more accurate and efficient anomaly detection in diverse application do-
mains.
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Language |
English |
Subject |
AutoML |
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Time series analysis |
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Time series classification |
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Ανάλυση χροσειρών |
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Αυτόματη μηχανική μάθηση |
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Ταξινόμηση χρονοσειρών |
Issue date |
2023-07-21 |
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/e/7/9/metadata-dlib-1688626100-88732-25782.tkl
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Views |
701 |