Conformalized density- and distance-based anomaly detection in time-series data
This work addresses anomaly detection for time-series applications like healthcare and security, but it appears incremental as it builds on existing conformal methods.
The authors tackled the problem of detecting anomalies in one-dimensional time-series data by proposing new conformalized density- and distance-based algorithms, which combine feature extraction, scoring, and probabilistic interpretation to assess unusual patterns.
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.