Conformal $k$-NN Anomaly Detector for Univariate Data Streams

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Vladislav Ishimtsev, Alexander Bernstein, Evgeny Burnaev, Ivan Nazarov ;
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:213-227, 2017.

Abstract

Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal prediction paradigm. Despite its simplicity the method performs on par with complex prediction-based models on the Numenta Anomaly Detection benchmark and the Yahoo! S5 dataset.

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