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HYPEX: Hyperparameter Optimization in Time Series Anomaly Detection

Author:
Schmidl, Sebastian [DBLP] ;
Wenig, Phillip [DBLP] ;
Papenbrock, Thorsten [DBLP]
Abstract
In many domains, such as data cleaning, machine learning, pattern mining, or anomaly detection, a system’s performance depends significantly on the selected configuration hyperparameters. However, manual configuration of hyperparameters is particularly difficult because it requires an in-depth understanding of the problem at hand and the system’s internal behavior. While automatic methods for hyperparameter optimization exist, they require labeled training datasets and many trials to assess a system’s performance before the system can be applied to production data. Hence, automatic methods just shift the human effort from parameter optimization to the effort of labelling datasets, which is still complex and time-consuming. In this paper, we, therefore, propose a novel hyperparameter optimization framework called HYPEX that learns promising default parameters and explainable parameter rules from synthetically generated datasets, without the need for manually labeled datasets. HYPEX’ learned parameter model enables the easy adjustment of a system’s configuration to new, unlabeled, and unseen datasets. We demonstrate the capabilities of HYPEX in the context of time series anomaly detection because anomaly detection algorithms suffer from a general lack of labeled datasets and they are particularly sensitive to parameter changes. In our evaluation, we show that our hyperparameter suggestions on unseen data significantly improve an algorithm’s performance compared to existing manual hyperparameter optimization approaches and often are competitive to the optimal performance achieved with Bayesian optimization.
  • Citation
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Schmidl, S., Wenig, P. & Papenbrock, T., (2023). HYPEX: Hyperparameter Optimization in Time Series Anomaly Detection. In: König-Ries, B., Scherzinger, S., Lehner, W. & Vossen, G. (Hrsg.), BTW 2023. Gesellschaft für Informatik e.V.. DOI: 10.18420/BTW2023-22
@inproceedings{mci/Schmidl2023,
author = {Schmidl, Sebastian AND Wenig, Phillip AND Papenbrock, Thorsten},
title = {HYPEX: Hyperparameter Optimization in Time Series Anomaly Detection},
booktitle = {BTW 2023},
year = {2023},
editor = {König-Ries, Birgitta AND Scherzinger, Stefanie AND Lehner, Wolfgang AND Vossen, Gottfried} ,
doi = { 10.18420/BTW2023-22 },
publisher = {Gesellschaft für Informatik e.V.},
address = {}
}
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More Info

DOI: 10.18420/BTW2023-22
ISBN: 978-3-88579-725-8
xmlui.MetaDataDisplay.field.date: 2023
Language: en (en)
Content Type: Text/Conference Paper

Keywords

  • Time Series Anomaly Detection
  • Bayesian Optimization
  • Causal Discovery
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  • P331 - BTW2023- Datenbanksysteme für Business, Technologie und Web [80]

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About uns | FAQ | Help | Imprint | Datenschutz

Gesellschaft für Informatik e.V. (GI), Kontakt: Geschäftsstelle der GI
Diese Digital Library basiert auf DSpace.