bfast-package {bfast}R Documentation

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.

Additionally monitoring disturbances in BFAST-type models at the end of time series (i.e., in near real-time) is available: Based on a model for stable historical behaviour abnormal changes within newly acquired data can be detected. Different models are available for modeling the stable historical behavior. A season-trend model (with harmonic seasonal pattern) is used as a default in the regresssion modelling.


The package contains:

Package options

bfast uses the following options to modify the default behaviour:

By default, all three are enabled. See set_fallback_options() for a convenient interface for setting them all off for debugging purposes.


Verbesselt J, Zeileis A, Herold M (2012). “Near real-time disturbance detection using satellite image time series.” Remote Sensing of Environment, 123, 98–108. ISSN 0034-4257, doi: 10.1016/j.rse.2012.02.022,

Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010). “Detecting trend and seasonal changes in satellite image time series.” Remote Sensing of Environment, 114(1), 106–115. ISSN 0034-4257, doi: 10.1016/j.rse.2009.08.014,

Verbesselt J, Hyndman R, Zeileis A, Culvenor D (2010). “Phenological change detection while accounting for abrupt and gradual trends in satellite image time series.” Remote Sensing of Environment, 114(12), 2970–2980. ISSN 0034-4257, doi: 10.1016/j.rse.2010.08.003,

[Package bfast version 1.6.1 Index]