R/oddstream.R

In oddstream: Outlier Detection in Data Streams

#' oddstream: A package for Outlier Detection in Data Streams
#'
#' Rapid advances in hardware technology have enabled a wide range of physical objects, living  beings
#' and environments to be monitored using sensors attached to them. Over time these sensors generate
#' streams of time series data. Finding anomalous events in streaming time series data has become an
#' interesting research topic due to its wide range of possible applications such as: intrusion detection,
#' water contamination monitoring, machine health monitoring, etc. This package 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 proposed
#' framework 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) DOI:10.1080/10618600.2019.1617160.
#'
#' @seealso The core functions in this package: \code{\link{find_odd_streams}},  \code{\link{extract_tsfeatures}}, \code{\link{get_pc_space}},
#' \code{\link{set_outlier_threshold}}, \code{\link{gg_featurespace}}
#' @references
#' Clifton, D. A., Hugueny, S., & Tarassenko, L. (2011). Novelty detection with multivariate extreme value statistics.
#' Journal of signal processing systems, 65 (3),371-389.
#'
#' Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019). Anomaly detection in streaming nonstationary temporal data.
#' Journal of Computational and Graphical Statistics, 1-28. DOI:10.1080/10618600.2019.1617160.
#'
#' @note The name \code{oddstream} comes from \code{Outlier Detection in Data STREAMs}
#' @docType package
#' @name  oddstream
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