# Conformal k-NN Anomaly Detector for Univariate Data Streams

arXiv: https://arxiv.org/abs/1706.03412 · HTML: https://mlsanity.com/paper/1706.03412/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2017.

## Built on by (most cited first, 13 of 13)

- [Testing for Outliers with Conformal p-values](https://mlsanity.com/paper/2104.08279/) (2021)
- [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://mlsanity.com/paper/2305.14535/) (2023)
- [Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers](https://mlsanity.com/paper/2208.11111/) (2022)
- [Anomalous Edge Detection in Edge Exchangeable Social Network Models](https://mlsanity.com/paper/2109.12727/) (2021)
- [Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data](https://mlsanity.com/paper/2105.11886/) (2021)
- [A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/2109.09889/) (2021)
- [Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors](https://mlsanity.com/paper/2402.16388/) (2024)
- [Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold](https://mlsanity.com/paper/2407.10042/) (2024)
- [CODiT: Conformal Out-of-Distribution Detection in Time-Series Data](https://mlsanity.com/paper/2207.11769/) (2022)
- [A Survey of Anomaly Detection in In-Vehicle Networks](https://mlsanity.com/paper/2409.07505/) (2024)
- [Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression](https://mlsanity.com/paper/2104.06613/) (2021)
- [A task of anomaly detection for a smart satellite Internet of things system](https://mlsanity.com/paper/2403.14738/) (2024)
- [On the Adversarial Robustness of Learning-based Conformal Novelty Detection](https://mlsanity.com/paper/2510.00463/) (2025)

## 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.