BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

Abstract

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.

Kinematic and Geometric Characterization of the Vögelsberg Rockslide (Tyrol, Austria) by Means of MT-InSAR Data

“…BFAST is a time series change detection algorithm able to detect multiple breakpoints within a time series. It decomposes at first the original data Yt into trend (Tt), seasonal (St), and error (et) components (Figure 7) using Seasonal decomposition of Time series by Loess (STL) [27]. On those two separated components, an ordinary least-squares residual moving sum (OLS-MOSUM) test is performed in order to evaluate the existence of at least a break point.…”

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

“…This may be the limitation of linear modeling and the need for nonlinear modeling such as machine learning and neural networks. Change detection algorithms such as jumps upon spectrum and trend (JUST) [51], breaks for additive season and trend (BFAST) [52], and continuous change detection and classification (CCDC) [53] may be utilized for abrupt change detections and better trend estimations. On the other hand, the RRK model showed a better estimate of 21.38 • C with only a 0.05 • C difference from the in situ measurement.…”

Detection of Abnormal Data in GNSS Coordinate Series Based on an Improved Cumulative Sum

“…Subsequently, Bakir and Reynolds [20], Qiu and Hawkins [21], and other scholars made improvements to this method [22,23]. Methods such as nonparametric CUSUM control charts and multivariate nonparametric CUSUM control charts have been proposed [20,21,24,25].…”