mlsanityData to 7 Oct 2026

Conformal k-NN Anomaly Detector for Univariate Data Streams

arXiv 1706.03412 ↗First posted 11 Jun 2017stat.ML cs.DS stat.APcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2017.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 1; 2024: 4; 2025: 1.

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