Titlebook: Singular Spectrum Analysis for Time Series; Nina Golyandina,Anatoly Zhigljavsky Book 2020Latest edition The Author(s), under exclusive lic

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2191-544X essing (2D-SSA).Illustrated with examples and case studiesThis book gives an overview of singular spectrum analysis (SSA). SSA is a technique of time series analysis and forecasting  combining  elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical syst

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Introduction,ies analysis and signal processing. The R-based package Rssa, a powerful and comprehensive implementation of SSA-related techniques, is introduced. A list of the main symbols and several historical and bibliographical remarks conclude Chap. 1.

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SSA for Forecasting, Interpolation, Filtering and Estimation,ion that the components of the original time series, which are extracted by SSA, satisfy (at least, locally) certain linear recurrence relations. The main emphasis in Chap. 3 is on time series forecasting and different methods of checking stability and adequacy of forecasts. Other related problems s

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Book 2020Latest editionassical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems

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Basic SSA,ifferent matrix norms, as well as the use of prior and posterior information. Chapter 2 concludes with a description of multidimensional and multivariate extensions of SSA, which are applied to collections of time series and digital images respectively.

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SSA for Forecasting, Interpolation, Filtering and Estimation,uch as imputation of missing values, interpolation and filtering are examined. Chapter 3 also surveys methods of parameter estimation of the models; such methods are very popular in signal processing. Chapter 3 concludes with descriptions of model-based extensions of multivariate and multidimensional SSA.

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