An iterative algorithm for automatic fitting of continuous piecewise linear models
An iterative algorithm for automatic fitting of continuous piecewise linear models
复制标题
连续分段线性模型自动拟合的迭代算法
DOI：
10.1016/j.ecosta.2021.07.007
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Francisco Rodríguez
中科院分区：
文献类型：
--
作者：
Miguel A. García;Francisco Rodríguez
连续分段线性模型是提取复杂时间序列数据增长模式基本特征的有效工具。在这项工作中，我们提出了一个自动变点估计的连续分段回归迭代算法。该算法需要对变点或铰链的数量和位置进行初始猜测，这可以通过不同的方法来获得，然后通过类似于牛顿算法的位移迭代地调整这些铰链来进行函数求根。该算法可以应用于大量的数据，在大多数情况下具有非常快的收敛速度，并且还允许识别足够接近的铰链，从而减少变点的数量，从而导致模型的低复杂性。介绍了从遥感植被指数时间序列数据中提取特征的应用实例。
Continuous piecewise linear models constitute useful tools to extract the basic features about the patterns of growth in complex time series data. In this work, we present an iterative algorithm for continuous piecewise regression with automatic change-points estimation. The algorithm requires an initial guess about the number and positions of the change-points or hinges, which can be obtained with different methods, and then proceeds by iteratively adjusting these hinges by displacements similar to those of Newton algorithm for function root finding. The algorithm can be applied to high volumes of data, with very fast convergence in most cases, and also allows for sufficiently close hinges to be identified, thus reducing the number of change-points, and so resulting in models of low complexity. Examples of applications to feature extraction from remote sensing vegetation indices time series data are presented.