Conformal k-NN Anomaly Detector for Univariate Data Streams
This work addresses anomaly detection for applications relying on time-series data, but it is incremental as it adapts existing conformal prediction to a specific domain.
The paper tackled anomaly detection in univariate time-series data streams by proposing a model-free method that adapts to non-stationarity and provides probabilistic abnormality scores, achieving performance on par with complex prediction-based models on benchmarks like Numenta Anomaly Detection and Yahoo! S5.
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.