Learning Ensembles of Anomaly Detectors on Synthetic Data

Dmitry Smolyakov, Nadezda Sviridenko, Vladislav Ishimtsev, Evgeny Burikov, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationAdvances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
    EditorsHuchuan Lu, Huajin Tang, Zhanshan Wang
    PublisherSpringer Verlag
    Pages292-306
    Number of pages15
    ISBN (Print)9783030228071
    DOIs
    Publication statusPublished - 2019
    Event16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation
    Duration: 10 Jul 201912 Jul 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11555 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference16th International Symposium on Neural Networks, ISNN 2019
    Country/TerritoryRussian Federation
    CityMoscow
    Period10/07/1912/07/19

    Keywords

    • Anomaly detection
    • Predictive maintenance
    • RWIS

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