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Titlebook: Automatic trend estimation; C?alin Vamos?,Maria Cr?aciun Book 2013 The Author(s) 2013 Automatic Estimation of Trends.Average Conditional D

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發(fā)表于 2025-3-21 16:07:21 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Automatic trend estimation
影響因子2023C?alin Vamos?,Maria Cr?aciun
視頻videohttp://file.papertrans.cn/167/166462/166462.mp4
發(fā)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
學科分類SpringerBriefs in Physics
圖書封面Titlebook: Automatic trend estimation;  C?alin Vamos?,Maria Cr?aciun Book 2013 The Author(s) 2013 Automatic Estimation of Trends.Average Conditional D
影響因子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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Dirichlet branches bifurcating from zero,series are introduced in Sect.?1.2. In the last section we discuss the properties of the order one autoregressive stochastic process AR(1) which has the serial correlation described by a single parameter and which is a good first approximation for many noises encountered in real phenomena.
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https://doi.org/10.1007/978-3-8349-8174-5e series with nonmonotonic trends the ACD algorithm determines one of the possible monotonic components which can be associated to the trend. As an illustration we apply the ACD algorithm to a paleoclimatic time series to determine the periods with a significant monotonic temperature variation.
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