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Singular spectrum analysis : using R / Hossein Hassani, Rahim Mahmoudvand

Author
Hassani, Hossein
Published
London, United Kingdom : Palgrave Macmillan, [2018]
Copyright Date
©2018
Physical Description
xiii, 149 pages : illustrations ; 22 cm.
Additional Creators
Mahmoudvand, Rahim

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Contents
Machine generated contents note: 1.Univariate Singular Spectrum Analysis -- 1.1.Introduction -- 1.2.Filtering and Smoothing -- 1.3.Comparing SSA and PCA -- 1.4.Choosing Parameters in SSA -- 1.4.1.Window Length -- 1.4.2.Grouping -- 1.5.Forecasting by SSA -- 1.5.1.Recurrent Forecasting Method -- 1.5.2.Vector Forecasting Method -- 1.5.3.A Theorctical Comparison of RSSA and VSSA -- 1.6.Automated SSA -- 1.6.1.Sensitivity Analysis -- 1.7.Prediction Interval for SSA -- 1.8.Two Real Data Analysis by SSA -- 1.8.1.UK Gas Consumption -- 1.8.2.The Real Yield on UK Government Security -- 1.9.Conclusion -- 2.Multivariate Singular Spectrum Analysis -- 2.1.Introduction -- 2.2.Filtering by MSSA -- 2.2.1.MSSA: Horizontal Form (HMSSA) -- 2.2.2.MSSA: Vertical Form (VMSSA) -- 2.3.Choosing Parameters in MSSA -- 2.3.1.Window Length (s) -- 2.3.2.Grouping Parameter, r -- 2.4.Forecasting by MSSA -- 2.4.1.HMSSA Recurrent Forecasting Algorithm (HMSSA-R) -- 2.4.2.VMSSA Recurrent Forecasting Algorithm (VMSSA-R) -- 2.4.3.HMSSA Vector Forecasting Algorithm (HMSSA-V) -- 2.4.4.VMSSA Vector Forecasting Algorithm (VMSSA-V) -- 2.5.Automated MSSA -- 2.5.1.MSSA Optimal Forecasting Algorithm -- 2.5.2.Automated MSSA R Code -- 2.6.A Real Data Analysis with MSSA -- 3.Applications of Singular Spectrum Analysis -- 3.1.Introduction -- 3.2.Change Point Detection -- 3.2.1.A Simple Change Point Detection Algorithm -- 3.2.2.Change-Point Detection R Code -- 3.3.Gap Filling with SSA -- 3.4.Demising by SSA -- 3.4.1.Filter Based Correlation Coefficients -- 4.More on Filtering and Forecasting by SSA -- 4.1.Introduction -- 4.2.Filtering Coefficients -- 4.3.Forecast Equation -- 4.3.1.Recurrent SSA Forecast Equation -- 4.3.2.Vector SSA Forecast Equation -- 4.4.Different Window Length for Forecasting and Reconstruction -- 4.5.Outlier in SSA.
Summary
"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."--
Subject(s)
ISBN
1137409509 (hardcover)
9781137409508 (hardcover)
Bibliography Note
Includes bibliographical references and index.
View MARC record | catkey: 24439523