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<p>Heteroscedastic time series exhibit non-constant variance over time, which undermines the effectiveness of classical anomaly detection methods based on fixed thresholds. In particular, it affects the identification of contextual anomalies whose detection depends on local variability. To address this challenge, this paper proposes the resilient transformation (RT), which modifies the original series through complementary ensemble empirical mode decomposition, selection of high-frequency components based on roughness, differentiation, and normalization by local dispersion. This transformation equalizes variance and highlights pointwise deviations. Evaluations using the Yahoo! S5 dataset, with the Harbinger framework, show that RT improves the performance of classical methods and can be combined with thresholding to form an autonomous detector (<i>detection method</i>) named anomaly detector by resilient transformation.</p>

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RT: resilient transformation for anomaly detection in heteroscedastic time series

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

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  • Laís Baroni,
  • Carlos Eduardo Mello,
  • Eduardo Mendes,
  • Esther Pacitti,
  • Fabio Porto,
  • Gustavo Guedes,
  • Rafaelli Coutinho,
  • Eduardo Ogasawara

摘要

Heteroscedastic time series exhibit non-constant variance over time, which undermines the effectiveness of classical anomaly detection methods based on fixed thresholds. In particular, it affects the identification of contextual anomalies whose detection depends on local variability. To address this challenge, this paper proposes the resilient transformation (RT), which modifies the original series through complementary ensemble empirical mode decomposition, selection of high-frequency components based on roughness, differentiation, and normalization by local dispersion. This transformation equalizes variance and highlights pointwise deviations. Evaluations using the Yahoo! S5 dataset, with the Harbinger framework, show that RT improves the performance of classical methods and can be combined with thresholding to form an autonomous detector (detection method) named anomaly detector by resilient transformation.