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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C˘alin Vamos¸,Maria Cr˘aciunThe 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

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SpringerBriefs in Physicshttp://image.papertrans.cn/b/image/166462.jpg

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Dirichlet branches bifurcating from zero,ntroductory chapter we briefly present some basic notions which are used in the rest of the book. The main methods to estimate trends from noisy time 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 t

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https://doi.org/10.1007/BFb0099278 a single parameter, the semi-length . of the averaging window. We introduce the repeated central moving average (RCMA) which depends on an additional parameter (the number . of averagings) and allows a gradual smoothing of the time series. Using Monte Carlo experiments we analyze the properties of

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