Breaks for Additive Season and Trend
Deprecated functions in package bfast.
Breaks For Additive Season and Trend (BFAST)
Break Detection in the Seasonal and Trend Component of a Univariate Ti...
Checking for one major break in the time series
Change type analysis of the bfast01 function
Detect multiple breaks in a time series
Near Real-Time Disturbance Detection Based on BFAST-Type Models
Time Series Preprocessing for BFAST-Type Models
Create a regular time series object by combining data and date informa...
A helper function to create time series
A vector with date information (a Datum type) to be linked with each N...
For all elements of a vector a, find the closest elements in a vector ...
A raster datacube of 16-day satellite image NDVI time series for a sma...
Methods for objects of class "bfast".
Plot the time series and results of BFAST Lite
Set package options with regard to computation times
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).