Title: Conformal k-NN Anomaly Detector for Univariate Data Streams,
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.