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) <doi:10.1080/10618600.2019.1617160>.

Allows performing forwards prediction for the General Unified 
          Threshold model of Survival using compiled ode code. This package 
          was created to avoid dependency with the 'morse' package that requires 
          the installation of 'JAGS'. This package is based on functions from 
          the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, 
          Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, 
          Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, 
          Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.

An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.