| 期刊全称 | Automatic trend estimation | | 影响因子2023 | C˘alin Vamos¸,Maria Cr˘aciun | | 视频video | | | 发行地址 | The reader will be able to reproduce the original automatic algorithms for trend estimation and time series partitioning.Teaches the essential characteristics of the polynomial fitting and moving aver | | 学科分类 | SpringerBriefs in Physics | | 图书封面 |  | | 影响因子 | 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. | | Pindex | Book 2013 |
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发表于 2025-3-21 16:07:21
