Breaks for Additive Seasonal and Trend

• Category: Deterministic Change Analysis
• Development language: R
• Operating platform / Operating system: Linux\Windows
• Compilation tools and environment: R
• Version: 1.5.7
• GitHub download path: https://gitee.com/three-poles/Time-series-analysis

Method description:

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 monitor provides functionality to detect disturbance in near real-time based on BFAST-type models.

Installation: need R environment;

Input: time series data

Output: the fited trend component,the fited seasonal component, the noise or remainder component;

QR code:

• 安装方式：安装R

  运行方式：在R环境中运行

  输入变量：时间序列数据

  输出变量：趋势项、周期项、残差项

  二维码：