  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.
