Decomposition of time series into
trend, seasonal, and remainder components with methods for detecting and
characterizing abrupt changes within the trend and seasonal components. 'BFAST'
can be used to analyze different types of satellite image time series and can
be applied to other disciplines dealing with seasonal or non-seasonal time
series, such as hydrology, climatology, and econometrics. The algorithm can be
extended to label detected changes with information on the parameters of the
fitted piecewise linear models. 'BFAST' monitoring functionality is described
in Verbesselt et al. (2010)
Changes in Version 1.5-7
o all required packages are now in imports so you have to load the package e.g. zoo yourself now.
Changes in Version 1.5-5
o Bfast01 classification function added
Changes in Version 1.5
o Bfast01 function added
Changes in Version 1.4-4
o Bfastmonitor function added
Changes in Version 1.4-3
o Preparing helper functions for processing of different types of time series data o Preparing structure and plan for raster brick processing (satellite image time series processing)
Changes in Version 1.4-1
o Plotting functionality is improved for bfastmonitor() output (i.e. when dealing with daily data and lot's of missing data points)
Changes in Version 1.4-0
o Added bfastmonitor() for near real-time detection of breaks in BFAST-type model. Data pre-processing is handled by a new function bfastpp() whose results can easily be plugged into strucchange (or other modeling/testing functions).
o New data set "som" with NDVI series from Somalia.