# Conformalized density- and distance-based anomaly detection in time-series data

arXiv: https://arxiv.org/abs/1608.04585 · HTML: https://mlsanity.com/paper/1608.04585/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems](https://mlsanity.com/paper/2102.11450/) (2021)
- [Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models](https://mlsanity.com/paper/2601.20367/) (2026)
- [Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care](https://mlsanity.com/paper/2305.09907/) (2023)
- [Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data](https://mlsanity.com/paper/2609.13940/) (2026)

## Abstract (arXiv)

Anomalies (unusual patterns) in time-series data give essential, and often actionable information in critical situations. Examples can be found in such fields as healthcare, intrusion detection, finance, security and flight safety. In this paper we propose new conformalized density- and distance-based anomaly detection algorithms for a one-dimensional time-series data. The algorithms use a combination of a feature extraction method, an approach to assess a score whether a new observation differs significantly from a previously observed data, and a probabilistic interpretation of this score based on the conformal paradigm.