Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly
Detection
- URL: http://arxiv.org/abs/2308.08915v2
- Date: Fri, 25 Aug 2023 09:01:23 GMT
- Title: Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly
Detection
- Authors: Haotian Si, Changhua Pei, Zhihan Li, Yadong Zhao, Jingjing Li, Haiming
Zhang, Zulong Diao, Jianhui Li, Gaogang Xie, Dan Pei
- Abstract summary: We introduce CAD, a Conflict-aware Anomaly Detection algorithm.
We find that the poor performance of vanilla MMoE mainly comes from the input-output misalignment settings of MTS formulation.
We show that CAD obtains an average F1-score of 0.943 across three public datasets, notably outperforming state-of-the-art methods.
- Score: 18.796225184893874
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Massive key performance indicators (KPIs) are monitored as multivariate time
series data (MTS) to ensure the reliability of the software applications and
service system. Accurately detecting the abnormality of MTS is very critical
for subsequent fault elimination. The scarcity of anomalies and manual labeling
has led to the development of various self-supervised MTS anomaly detection
(AD) methods, which optimize an overall objective/loss encompassing all
metrics' regression objectives/losses. However, our empirical study uncovers
the prevalence of conflicts among metrics' regression objectives, causing MTS
models to grapple with different losses. This critical aspect significantly
impacts detection performance but has been overlooked in existing approaches.
To address this problem, by mimicking the design of multi-gate
mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI
Anomaly Detection algorithm. CAD offers an exclusive structure for each metric
to mitigate potential conflicts while fostering inter-metric promotions. Upon
thorough investigation, we find that the poor performance of vanilla MMoE
mainly comes from the input-output misalignment settings of MTS formulation and
convergence issues arising from expansive tasks. To address these challenges,
we propose a straightforward yet effective task-oriented metric selection and
p&s (personalized and shared) gating mechanism, which establishes CAD as the
first practicable multi-task learning (MTL) based MTS AD model. Evaluations on
multiple public datasets reveal that CAD obtains an average F1-score of 0.943
across three public datasets, notably outperforming state-of-the-art methods.
Our code is accessible at https://github.com/dawnvince/MTS_CAD.
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