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).
- Jan Verbesselt [aut, cre], Achim Zeileis [aut], Rob Hyndman [ctb]
- Date of publication
- 2014-08-28 00:00:24
- Jan Verbesselt <Jan.Verbesselt@wur.nl>
- GPL (>= 2)
- Break Detection in the Seasonal and Trend Component of a...
- Checking for one major break in the time series
- Change type analysis of the bfast01 function
- Near Real-Time Disturbance Detection Based on BFAST-Type...
- Breaks For Additive Season and Trend (BFAST)
- Time Series Preprocessing for BFAST-Type Models
- Create a regular time series object by combining data and...
- A helper function to create time series
- A vector with date information (a Datum type) to be linked...
- 16-day NDVI time series for a Pinus radiata plantation.
- A raster brick of 16-day satellite image NDVI time series for...
- A random NDVI time series
- Methods for objects of class "bfast".
- Simulated seasonal 16-day NDVI time series
- Two 16-day NDVI time series from the south of Somalia
Files in this package