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cff-version: 1.2.0
message: 'To cite package "oddstream" in publications use:'
type: software
license: GPL-3.0-only
title: 'oddstream: Outlier Detection in Data Streams'
version: 0.5.1
doi: 10.32614/CRAN.package.oddstream
abstract: We proposes a framework that provides real time support for early detection
  of anomalous series within a large collection of streaming time series data. By
  definition, anomalies are rare in comparison to a system's typical behaviour. We
  define an anomaly as an observation that is very unlikely given the forecast distribution.
  The algorithm first forecasts a boundary for the system's typical behaviour using
  a representative sample of the typical behaviour of the system. An approach based
  on extreme value theory is used for this boundary prediction process. Then a sliding
  window is used to test for anomalous series within the newly arrived collection
  of series. Feature based representation of time series is used as the input to the
  model. To cope with concept drift, the forecast boundary for the system's typical
  behaviour is updated periodically. More details regarding the algorithm can be found
  in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) <https://doi.org/10.1080/10618600.2019.1617160>.
authors:
- family-names: Talagala
  given-names: Priyanga Dilini
  email: pritalagala@gmail.com
repository: https://robjhyndman.r-universe.dev
repository-code: https://github.com/pridiltal/oddstream
commit: acb95325e27b475fb6c91151cf6f3d18ca622563
url: https://github.com/pridiltal/oddstream
contact:
- family-names: Talagala
  given-names: Priyanga Dilini
  email: pritalagala@gmail.com