bfast

Description

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).

Downloads

Trend: +4.3% (30d vs prior 30d)

CRAN Check Status

Check History

Comparing ‘bfast-Ex.Rout’ to ‘bfast-Ex.Rout.save’:
624c624
< -0.153442 -0.030025 -0.001339  0.020911  0.213985 
---
> -0.153435 -0.030022 -0.001384  0.021004  0.214001 
628,630c628,630
< (Intercept) 0.0003619  0.0060051   0.060   0.9521  
< lag1     
...[truncated]...
  0.1045727   2.109   0.0379 *
636c636
< F-statistic: 8.444 on 2 and 87 DF,  p-value: 0.000445
---
> F-statistic: 8.446 on 2 and 87 DF,  p-value: 0.0004445
645c645
< 0.0003618779 0.2697706795 0.2204647235 
---
> 0.0003608327 0.2697746828 0.2204983802

Reverse Dependencies (2)

Dependency Network

Version History