Singular Spectrum Analysis Using R /

This book provides a broad introduction to computational aspects of Singular Spectrum Analysis (SSA) which is a non-parametric technique and requires no prior assumptions such as stationarity, normality or linearity of the series. This book is unique as it not only details the theoretical aspects un...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Hassani, Hossein (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Mahmoudvand, Rahim (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : Palgrave Macmillan UK : Imprint: Palgrave Pivot, 2018.
Έκδοση:1st ed. 2018.
Σειρά:Palgrave Advanced Texts in Econometrics,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Singular Spectrum Analysis  |h [electronic resource] :  |b Using R /  |c by Hossein Hassani, Rahim Mahmoudvand. 
250 |a 1st ed. 2018. 
264 1 |a London :  |b Palgrave Macmillan UK :  |b Imprint: Palgrave Pivot,  |c 2018. 
300 |a XIII, 149 p. 40 illus.  |b online resource. 
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490 1 |a Palgrave Advanced Texts in Econometrics,  |x 2662-639X 
505 0 |a Preface -- 1. Univariate Singular Spectrum Analysis -- 2. Multivariate Singular Spectrum Analysis -- 3. Applications of Singular Spectrum Analysis -- 4. More on Filtering and Forecasting by SSA -- A. A Short Introduction to R -- B. Theoretical explanations. . 
520 |a This book provides a broad introduction to computational aspects of Singular Spectrum Analysis (SSA) which is a non-parametric technique and requires no prior assumptions such as stationarity, normality or linearity of the series. This book is unique as it not only details the theoretical aspects underlying SSA, but also provides a comprehensive guide enabling the user to apply the theory in practice using the R software. Further, it provides the user with step- by- step coding and guidance for the practical application of the SSA technique to analyze their time series databases using R. The first two chapters present basic notions of univariate and multivariate SSA and their implementations in R environment. The next chapters discuss the applications of SSA to change point detection, missing-data imputation, smoothing and filtering. This book is appropriate for researchers, upper level students (masters level and beyond) and practitioners wishing to revive their knowledge of times series analysis or to quickly learn about the main mechanisms of SSA. . 
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