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Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection

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Byung Suk LeeAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 4204 - 4215

https://doi.org/10.1145/3589334.3645556

Published: 13 May 2024 Publication History

Abstract

In light of the remarkable advancements made in time-series anomaly detection(TSAD), recent emphasis has been placed on exploiting the frequency domain as well as the time domain to address the difficulties in precisely detecting pattern-wise anomalies. However, in terms of anomaly scores, the window granularity of the frequency domain is inherently distinct from the data-point granularity of the time domain. Owing to this discrepancy, the anomaly information in the frequency domain has not been utilized to its full potential for TSAD. In this paper, we propose a TSAD framework, Dual-TF, that simultaneously uses both the time and frequency domains while breaking the time-frequency granularity discrepancy. To this end, our framework employs nested-sliding windows, with the outer and inner windows responsible for the time and frequency domains, respectively, and aligns the anomaly scores of the two domains. As a result of the high resolution of the aligned scores, the boundaries of pattern-wise anomalies can be identified more precisely. In six benchmark datasets, our framework outperforms state-of-the-art methods by 12.0--147%, as demonstrated by experimental results.

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Cited By

Wang FJiang YZhang RWei AXie JPang X(2025)A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and DirectionsSensors10.3390/s2501019025:1(190)Online publication date: 1-Jan-2025
https://doi.org/10.3390/s25010190
Yang BMa TRong HHuang XWang YZhao BWang C(2025)TADST: reconstruction with spatio-temporal feature fusion for deviation-based time series anomaly detectionApplied Intelligence10.1007/s10489-025-06310-x55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06310-x
Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/3691338Online publication date: 30-Aug-2024
https://doi.org/10.1145/3691338

Index Terms

Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
    2. Machine learning algorithms
      1. Spectral methods
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 Owner/Author.

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

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Published: 13 May 2024

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May 13 - 17, 2024

Singapore, Singapore

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Cited By

Wang FJiang YZhang RWei AXie JPang X(2025)A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and DirectionsSensors10.3390/s2501019025:1(190)Online publication date: 1-Jan-2025
https://doi.org/10.3390/s25010190
Yang BMa TRong HHuang XWang YZhao BWang C(2025)TADST: reconstruction with spatio-temporal feature fusion for deviation-based time series anomaly detectionApplied Intelligence10.1007/s10489-025-06310-x55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06310-x
Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/3691338Online publication date: 30-Aug-2024
https://doi.org/10.1145/3691338

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