Automatic trend estimation
Automatic trend estimation
复制标题
自动趋势估计
DOI：
10.1007/978-94-007-4825-5
复制
发表时间：
2012
影响因子：
49.6
通讯作者：
M. Craciun
中科院分区：
文献类型：
--
作者：
C. Vamos;M. Craciun
我们的书介绍了一种在类似于实时序列处理中遇到的条件下评估趋势估计算法准确性的方法。该方法基于蒙特卡罗实验，采用原始算法数值生成的人工时间序列。本书的第二部分包含几种用于趋势估计和时间序列划分的自动算法。实现这些原始自动算法的计算机程序的源代码在附录中给出，并将在网络上免费提供。本书清晰地阐述了算法工作的条件和近似值，以及对其结果的正确解释。我们通过处理天体物理学、金融学、生物物理学和古气候学的时间序列来说明所分析算法的功能。我们书中广泛使用的数值实验方法已经在计算和统计物理学中得到普遍使用。
Our book introduces a method to evaluate the accuracy of trend estimation algorithms under conditions similar to those encountered in real time series processing. This method is based on Monte Carlo experiments with artificial time series numerically generated by an original algorithm. The second part of the book contains several automatic algorithms for trend estimation and time series partitioning. The source codes of the computer programs implementing these original automatic algorithms are given in the appendix and will be freely available on the web. The book contains clear statement of the conditions and the approximations under which the algorithms work, as well as the proper interpretation of their results. We illustrate the functioning of the analyzed algorithms by processing time series from astrophysics, finance, biophysics, and paleoclimatology. The numerical experiment method extensively used in our book is already in common use in computational and statistical physics.