Titlebook: Singular Spectrum Analysis with R; Nina Golyandina,Anton Korobeynikov,Anatoly Zhiglja Book 2018 Springer-Verlag GmbH Germany, part of Spri

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書目名稱Singular Spectrum Analysis with R影響因子(影響力)




書目名稱Singular Spectrum Analysis with R影響因子(影響力)學(xué)科排名




書目名稱Singular Spectrum Analysis with R網(wǎng)絡(luò)公開度




書目名稱Singular Spectrum Analysis with R網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Singular Spectrum Analysis with R被引頻次




書目名稱Singular Spectrum Analysis with R被引頻次學(xué)科排名




書目名稱Singular Spectrum Analysis with R年度引用




書目名稱Singular Spectrum Analysis with R年度引用學(xué)科排名




書目名稱Singular Spectrum Analysis with R讀者反饋




書目名稱Singular Spectrum Analysis with R讀者反饋學(xué)科排名

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CRUMB

Parameter Estimation, Forecasting, Gap Filling, change-point detection. The SSA analysis of time series of Chap. . is model-free. Methods of Chap. 3, on the contrary, are model-based. The model is constructed on the base of the approximating subspace built in the process of performing the SSA analysis of Chap. .. The main parametric model is a l

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減至最低

燈泡

交響樂

SSA Analysis of One-Dimensional Time Series,components such as trend, seasonality, and noise are thoroughly discussed and illustrated on case studies with real data. An important issue of automatization of the SSA methods is also considered in detail.

障礙

Image Processing, The third temporal dimension naturally arises if images are changing in time. The Rssa package implements the so-called nD-SSA for analysis of objects of arbitrary dimensions, in rectangular and shaped versions. Several examples of this chapter demonstrate that Rssa can be efficiently applied to very complex problems of image processing.

多山

Munificent

Introduction: Overview,ata sources used. In this chapter, the main concepts and generic structure of all methods of the book are introduced and explained; hence, the material of Chap. 1 is essential for the rest of the book.