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CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge

Published: 24 August 2024 Publication History

Abstract

Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.

Supplemental Material

MP4 File - CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge
Welcome to our promo video where we unveil the intuition behind CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples to approximate five kinds of anomalies, including point (global and contextual) anomalies and pattern (shaplete / correlation, season, and trend) ones. By incorporating domain knowledge, CutAddPaste enables the model to understand and detect diverse anomalies and improve overall performance.

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cover image ACM Conferences
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
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Published: 24 August 2024

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Author Tags

  1. abnormal knowledge
  2. anomaly detection
  3. anomaly-assumption
  4. data augmentation
  5. time series

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