ML Anthology

Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses

Youngeun Nam, Patara Trirat, Taeyoon Kim, Youngseop Lee, Jae-Gil Lee
ECML-PKDD 2023 pp. 330-345
doi:10.1007/978-3-031-43427-3_20 /ecmlpkdd/2023/nam2023ecmlpkdd-contextaware/

Abstract

Detecting anomalies in time series has become increasingly challenging as data collection technology develops, especially in real-world communication services, which require contextual information for precise prediction. To address this challenge, researchers usually use time-series decomposition to reveal underlying patterns, e.g., trends and seasonality. However, existing decomposition-based anomaly detectors do not explicitly consider such contextual information , limiting their ability to correctly detect contextual cases. This paper proposes Time-CAD , a new context-aware deep time-series decomposition framework to detect anomalies for a more practical scenario in real-world businesses. We verify the effectiveness of the novel design for integrating contextual information into deep time-series decomposition through extensive experiments on four real-world benchmarks, demonstrating improvements of up to $46\%$ in time-series aware $F_1$ score on average.

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Cite

Text

Nam et al. "Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43427-3_20

Markdown

[Nam et al. "Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/nam2023ecmlpkdd-contextaware/) doi:10.1007/978-3-031-43427-3_20

BibTeX

@inproceedings{nam2023ecmlpkdd-contextaware,
  title     = {{Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses}},
  author    = {Nam, Youngeun and Trirat, Patara and Kim, Taeyoon and Lee, Youngseop and Lee, Jae-Gil},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2023},
  pages     = {330-345},
  doi       = {10.1007/978-3-031-43427-3_20},
  url       = {https://mlanthology.org/ecmlpkdd/2023/nam2023ecmlpkdd-contextaware/}
}