bfast: Breaks For Additive Season and Trend (BFAST)

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).

Version: 1.5.7
Depends: R (≥ 2.15.0)
Imports: graphics, stats, strucchange, zoo, forecast, sp, raster
Published: 2014-08-28
Author: Jan Verbesselt [aut, cre], Achim Zeileis [aut], Rob Hyndman [ctb]
Maintainer: Jan Verbesselt <Jan.Verbesselt at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: bfast citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: bfast results


Reference manual: bfast.pdf
Package source: bfast_1.5.7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: bfast_1.5.7.tgz, r-oldrel: bfast_1.5.7.tgz
Old sources: bfast archive

Reverse dependencies:

Reverse imports: TSS.RESTREND


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