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We present a prototype real-time pipeline for anomaly detection and explanation in streaming time series data. The system consists of four core modules: time series storage, anomaly detection, model-agnostic explanation, and interactive visualisation. It supports multiple detection methods and produces variable-level contribution scores to explain real-time anomalies. An explainability score is introduced to quantify interpretability, aiding domain experts in understanding alerts. The pipeline updates both detection and explanation modules incrementally using a window-based approach. Preliminary results show reliable detection and intuitive explanations. Our pipeline is fully containerised and distributed to facilitate demonstration and deployment, enabling quick setup and reproducibility. Future work will focus on continuous learning to adapt to new data without forgetting past patterns.

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A Real-Time Pipeline for Anomaly Detection and Explanation in Streaming Data

  • 会议论文

  • 发表日期：

    2026年

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  • Florentin Jiechieu Kameni,
  • Anne Marthe Sophie Ngo Bibinbe,
  • Vasilis Cako,
  • Abdoul Jalil Djiberou Mahamadouc,
  • Mohamed Rayane Bakari,
  • Kevin Dilan Nguetche,
  • Durande Kamga Nguifo,
  • Anthony Bertrand,
  • Michael F. Mbouopda,
  • Rim El Cheikh,
  • Gertrude Raissa Mbiadou Saleu,
  • William Guyot Lenat,
  • Patrick Asante Owusu,
  • Engelbert Mephu Nguifo

摘要

We present a prototype real-time pipeline for anomaly detection and explanation in streaming time series data. The system consists of four core modules: time series storage, anomaly detection, model-agnostic explanation, and interactive visualisation. It supports multiple detection methods and produces variable-level contribution scores to explain real-time anomalies. An explainability score is introduced to quantify interpretability, aiding domain experts in understanding alerts. The pipeline updates both detection and explanation modules incrementally using a window-based approach. Preliminary results show reliable detection and intuitive explanations. Our pipeline is fully containerised and distributed to facilitate demonstration and deployment, enabling quick setup and reproducibility. Future work will focus on continuous learning to adapt to new data without forgetting past patterns.