BFAST integrates the 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 added based on a paper that has been submitted to Remote Sensing of Environment. BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models. BFAST approach is flexible approach that handles missing data without interpolation. Furthermore now different models can be used to fit the time series data and detect structural changes (breaks).
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.