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Table of Contents
Intro; Preface; Contents; List of Figures; List of Tables; 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 Theoretical 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 Analysis2.1 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 MSSA3 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 Denoising 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
A: A Short Introduction to RA.1 Beginning with R; A.2 Types of Data in R; A.3 Data Entry in R; A.4 Manipulating and Editing Data in R; A.5 Demonstration of R Functions; A.5.1 Computational Functions; A.5.2 Graphical Functions; A.5.3 Probability Distribution Functions and Their Components in R; A.5.4 Random Samples and Permutations in R; A.6 Writing New Functions in R; A.6.1 Loops and Conditions in R; B: Theoretical Explanations; References; : Index
2 Multivariate Singular Spectrum Analysis2.1 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 MSSA3 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 Denoising 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
A: A Short Introduction to RA.1 Beginning with R; A.2 Types of Data in R; A.3 Data Entry in R; A.4 Manipulating and Editing Data in R; A.5 Demonstration of R Functions; A.5.1 Computational Functions; A.5.2 Graphical Functions; A.5.3 Probability Distribution Functions and Their Components in R; A.5.4 Random Samples and Permutations in R; A.6 Writing New Functions in R; A.6.1 Loops and Conditions in R; B: Theoretical Explanations; References; : Index