Learning Ensembles of Anomaly Detectors on Synthetic Data
This addresses anomaly detection for meteorological data in road weather systems, but appears incremental as it combines existing ensemble and synthetic data techniques.
The researchers tackled the problem of automatic anomaly detection in meteorological time-series by developing an ensemble of anomaly detectors with adaptive threshold selection based on synthetic anomalies, and demonstrated its efficiency by integrating it into the 'Minimax-94' road weather information system.
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.