Singular Spectrum Analysis for Time Series

Introduction: Preliminaries.- SSA Methodology and the Structure of the Book.- SSA Topics Outside the Scope of this Book.- Common Symbols and Acronyms.- Basic SSA: The Main Algorithm.- Potential of Basic SSA.- Models of Time Series and SSA Objectives.- Choice of Parameters in Basic SSA.- Some Variations of Basic SSA.- SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms.- LRR and Associated Characteristic Polynomials.- Recurrent Forecasting as Approximate Continuation.- Confidence Bounds for the Forecast.- Summary and Recommendations on Forecasting Parameters.- Case Study: 'Fortified Wine'.- Missing Value Imputation.- Subspace-Based Methods and Estimation of Signal Parameters.- SSA and Filters.

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