bfast - Breaks for Additive Season and Trend
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) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor'
provides functionality to detect disturbance in near real-time
based on 'BFAST'- type models, and is described in Verbesselt
et al. (2012) <doi:10.1016/j.rse.2012.02.022>. 'BFAST Lite'
approach is a flexible approach that handles missing data
without interpolation, and will be described in an upcoming
paper. Furthermore, different models can now be used to fit the
time series data and detect structural changes (breaks).
