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Learning Ensembles of Anomaly Detectors on Synthetic Data

  • Dmitry Smolyakov
  • Nadezda Sviridenko
  • Vladislav Ishimtsev
  • Evgeny Burikov
  • Evgeny BurnaevEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

The main aim of this work is to develop and implement an automatic anomaly detection algorithm for meteorological time-series. To achieve this goal we develop an approach to constructing an ensemble of anomaly detectors in combination with adaptive threshold selection based on artificially generated anomalies. We demonstrate the efficiency of the proposed method by integrating the corresponding implementation into “Minimax-94” road weather information system.

Keywords

Anomaly detection Predictive maintenance RWIS 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dmitry Smolyakov
    • 1
  • Nadezda Sviridenko
    • 1
  • Vladislav Ishimtsev
    • 1
  • Evgeny Burikov
    • 2
  • Evgeny Burnaev
    • 1
    Email author
  1. 1.Skolkovo Institute of Science and TechnologyMoscow RegionRussia
  2. 2.PO–AO “Minimaks-94”MoscowRussia

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