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.

From the reviews:
"This book is fully devoted to the methodology of a technique for time series analysis and forecasting called singular spectrum analysis (SSA). ... The authors of the book are well-known statisticians, and specialists in time series analysis. ... this book is a valuable addition to the literature on time series analysis and will therefore be well received by statisticians and specialists in many other fields interested in the analysis of time series data." (K. S. Padmanabhan, zbMATH, Vol. 1276, 2014)