Context-aware Domain Adaptation for Time Series Anomaly Detection
- URL: http://arxiv.org/abs/2304.07453v1
- Date: Sat, 15 Apr 2023 02:28:58 GMT
- Title: Context-aware Domain Adaptation for Time Series Anomaly Detection
- Authors: Kwei-Herng Lai, Lan Wang, Huiyuan Chen, Kaixiong Zhou, Fei Wang, Hao
Yang, Xia Hu
- Abstract summary: Time series anomaly detection is a challenging task with a wide range of real-world applications.
Recent efforts have been devoted to time series domain adaptation to leverage knowledge from similar domains.
We propose a framework that combines context sampling and anomaly detection into a joint learning procedure.
- Score: 69.3488037353497
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Time series anomaly detection is a challenging task with a wide range of
real-world applications. Due to label sparsity, training a deep anomaly
detector often relies on unsupervised approaches. Recent efforts have been
devoted to time series domain adaptation to leverage knowledge from similar
domains. However, existing solutions may suffer from negative knowledge
transfer on anomalies due to their diversity and sparsity. Motivated by the
empirical study of context alignment between two domains, we aim to transfer
knowledge between two domains via adaptively sampling context information for
two domains. This is challenging because it requires simultaneously modeling
the complex in-domain temporal dependencies and cross-domain correlations while
exploiting label information from the source domain. To this end, we propose a
framework that combines context sampling and anomaly detection into a joint
learning procedure. We formulate context sampling into the Markov decision
process and exploit deep reinforcement learning to optimize the time series
domain adaptation process via context sampling and design a tailored reward
function to generate domain-invariant features that better align two domains
for anomaly detection. Experiments on three public datasets show promise for
knowledge transfer between two similar domains and two entirely different
domains.
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