Robust Learning of Deep Time Series Anomaly Detection Models with
Contaminated Training Data
- URL: http://arxiv.org/abs/2208.01841v1
- Date: Wed, 3 Aug 2022 04:52:08 GMT
- Title: Robust Learning of Deep Time Series Anomaly Detection Models with
Contaminated Training Data
- Authors: Wenkai Li, Cheng Feng, Ting Chen, Jun Zhu
- Abstract summary: Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era.
Deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics.
We propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data.
- Score: 29.808942473293108
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Time series anomaly detection (TSAD) is an important data mining task with
numerous applications in the IoT era. In recent years, a large number of deep
neural network-based methods have been proposed, demonstrating significantly
better performance than conventional methods on addressing challenging TSAD
problems in a variety of areas. Nevertheless, these deep TSAD methods typically
rely on a clean training dataset that is not polluted by anomalies to learn the
"normal profile" of the underlying dynamics. This requirement is nontrivial
since a clean dataset can hardly be provided in practice. Moreover, without the
awareness of their robustness, blindly applying deep TSAD methods with
potentially contaminated training data can possibly incur significant
performance degradation in the detection phase. In this work, to tackle this
important challenge, we firstly investigate the robustness of commonly used
deep TSAD methods with contaminated training data which provides a guideline
for applying these methods when the provided training data are not guaranteed
to be anomaly-free. Furthermore, we propose a model-agnostic method which can
effectively improve the robustness of learning mainstream deep TSAD models with
potentially contaminated data. Experiment results show that our method can
consistently prevent or mitigate performance degradation of mainstream deep
TSAD models on widely used benchmark datasets.
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