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).
|Author||Jan Verbesselt [aut, cre], Achim Zeileis [aut], Rob Hyndman [ctb]|
|Date of publication||2014-08-28 00:00:24|
|Maintainer||Jan Verbesselt <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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