bfast

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).

Last 30 days

This package has been downloaded 1,380 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 25 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 15,666 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was Oct 23, 2024 with 127 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.