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Learning Ensembles of Anomaly Detectors on Synthetic Data

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Advances in Neural Networks – ISNN 2019 (ISNN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11555))

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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.

The research was partially supported by the Russian Foundation for Basic Research grants 16-29-09649 ofi m.

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Author information

Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow Region, Russia

    Dmitry Smolyakov, Nadezda Sviridenko, Vladislav Ishimtsev & Evgeny Burnaev

  2. PO–AO “Minimaks-94”, Moscow, Russia

    Evgeny Burikov

Authors
  1. Dmitry Smolyakov
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  2. Nadezda Sviridenko
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  3. Vladislav Ishimtsev
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  4. Evgeny Burikov
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  5. Evgeny Burnaev
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Corresponding author

Correspondence to Evgeny Burnaev .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. Sichuan University, Chengdu, China

    Huajin Tang

  3. Northeastern University, Shenyang, China

    Zhanshan Wang

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Smolyakov, D., Sviridenko, N., Ishimtsev, V., Burikov, E., Burnaev, E. (2019). Learning Ensembles of Anomaly Detectors on Synthetic Data. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_30

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  • DOI: https://doi.org/10.1007/978-3-030-22808-8_30

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