Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark

@article{Lavin2015EvaluatingRA,
  title={Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark},
  author={Alexander Lavin and Subutai Ahmad},
  journal={2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)},
  year={2015},
  pages={38-44}
}
Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations, examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the… CONTINUE READING
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