Learning Ensembles of Anomaly Detectors on Synthetic Data

D. Smolyakov; N. Sviridenko; V. Ishimtsev; E. Burikov; E. Burnaev

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arXiv · 1905.07892

Learning Ensembles of Anomaly Detectors on Synthetic Data

D. Smolyakov·N. Sviridenko·V. Ishimtsev·E. Burikov·E. Burnaev

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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.

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D. Smolyakov, N. Sviridenko, V. Ishimtsev, E. Burikov, E. Burnaev. 2019-05-20. Learning Ensembles of Anomaly Detectors on Synthetic Data. https://arxiv.org/abs/1905.07892

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