(a) Main frame of the proof of concept (web app) applied on simulated data series (a.1). Illustration of the computation steps (a.2 and a.3) and the anomaly score (a.4). (b) Accuracy comparison between Series2graph (in blue) and other anomaly detection methods.


Automatically determine the health of a system, equipped with sensors, by detecting and graphically represent subsequence anomalies in a time series, without prior knowledge of the system.



  • Internet of Things
  • Operation monitoring: aeronautics, automobiles, railways
  • Industrial production site monitoring
  • Control systems such as SCADA
  • Health: monitoring physiological parameters
  • Finance: fraud detection
  • Computer data center operation health monitoring


Competitive advantages

  • No prior knowledge of the domain and anomaly characteristics
  • No need of labeled instances (unsupervised method)
  • Identification of anomalies of varying lengths
  • Identification of single and recurrent anomalies of various types
  • High accuracy and fast computing method


Intellectual property

  • Patent application filled on May 2020



Anomalies - Subsequence anomalies - Outliers Time series - Data series

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