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Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

Authors:

Dan PeiAuthors Info & Claims

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 1635 - 1645

https://doi.org/10.1145/3611643.3613896

Published: 30 November 2023 Publication History

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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Cited By

Wang ZPei CMa MWang XLi ZPei DRajmohan SZhang DLin QZhang HLi JXie GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveProceedings of the ACM Web Conference 202410.1145/3589334.3645710(3096-3105)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645710
Yu YOuyang YWu LLin CTsai K(2024)Multivariate Time-Series Anomaly Detection in IoT with a Bi-Dual GM GRU Autoencoder2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00106(746-754)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00106

Index Terms

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
    2. Machine learning approaches
      1. Neural networks

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Information & Contributors

Information

Published In

cover image ACM Conferences

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2023

2215 pages

ISBN:9798400703270

DOI:10.1145/3611643

General Chair:
Satish Chandra
Google, USA
,
Program Chairs:
Kelly Blincoe
University of Auckland, New Zealand
,
Paolo Tonella
USI Lugano, Switzerland

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 November 2023

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the National Key Research and Development Program of China
the National Natural Science Foundation of China
the State Key Program of National Natural Science of China
the CAS Program for fostering international mega-science

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ESEC/FSE '23

Sponsor:

SIGSOFT

ESEC/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

December 3 - 9, 2023

CA, San Francisco, USA

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Overall Acceptance Rate 112 of 543 submissions, 21%

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Cited By

Wang ZPei CMa MWang XLi ZPei DRajmohan SZhang DLin QZhang HLi JXie GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveProceedings of the ACM Web Conference 202410.1145/3589334.3645710(3096-3105)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645710
Yu YOuyang YWu LLin CTsai K(2024)Multivariate Time-Series Anomaly Detection in IoT with a Bi-Dual GM GRU Autoencoder2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00106(746-754)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00106

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