Singular Spectrum Analysis for Time Series

Singular Spectrum Analysis for Time Series Synopsis

This 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 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 arising in diverse areas. Rapidly  increasing number of novel applications of SSA is a consequence of the  new  fundamental research on SSA and  the recent progress in  computing and software engineering which  made it possible to use SSA for very complicated tasks that were unthinkable  twenty years ago. In this book, the methodology of SSA is concisely  but at the same time comprehensively explained by  two  prominent statisticians with huge experience in SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The second edition of the book contains many updates and some new material including a thorough discussion on  the place of SSA among other methods and new sections on multivariate and multidimensional extensions of  SSA.

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