BEAST（突变、季节性和趋势的贝叶斯估计器）是一种快速、通用的贝叶斯模型平均算法，用于将时间序列或1D序列数据分解为单个组件，例如突变、趋势和周期/季节性变化，如Zhao等人（2019）所述。BEAST可用于变化点检测（即断点或结构中断）、非线性趋势分析、时间序列分解和时间序列分割
BEAST是用C/C++实现的，但可以从R和Matlab访问。
BEAST (Bayesian Estimator of Abrupt change, Seasonality, and Trend) is a fast, generic Bayesian model averaging algorithm to decompose time series or 1D sequential data into individual components, such as abrupt changes, trends, and periodic/seasonal variations, as described in Zhao et al. (2019). BEAST is useful for changepoint detection (i.e., breakpoints or structural breaks), nonlinear trend analysis, time series decomposition, and time series segmentation
BEAST was impemented in C/C++ but accessible from R and Matlab.
参考文献：
Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick B., Zhang, X., & Brown, M. (2019). Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232, 111181. (the BEAST paper)
Zhao, K., Valle, D., Popescu, S., Zhang, X. and Mallick, B., 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, pp.102-119. (the mcmc sampler used for BEAST)
Hu, T., Toman, E.M., Chen, G., Shao, G., Zhou, Y., Li, Y., Zhao, K. and Feng, Y., 2021. Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 176, pp.250-261. (an application paper)
下载地址：
https://url92.ctfile.com/f/1850492-562577843-3ecb9d
（访问密码：3660）
