Ebook Download Singular Spectrum Analysis for Time Series (SpringerBriefs in Statistics), by Nina Golyandina, Anatoly Zhigljavsky
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Singular Spectrum Analysis for Time Series (SpringerBriefs in Statistics), by Nina Golyandina, Anatoly Zhigljavsky
Ebook Download Singular Spectrum Analysis for Time Series (SpringerBriefs in Statistics), by Nina Golyandina, Anatoly Zhigljavsky
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Singular spectrum analysis (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 seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity are assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. The present book is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis.
- Sales Rank: #1926607 in eBooks
- Published on: 2013-01-19
- Released on: 2013-01-19
- Format: Kindle eBook
Most helpful customer reviews
0 of 0 people found the following review helpful.
A clear and concise guide to using singular spectrum analysis
By Joshua Woods
This book is a great overview of singular spectrum analysis (SSA) and provides good references for much of its material. The book is short, concise, but easy to read. However, I did find that the book (Singular Spectrum Analysis: A New Tool in Time Series Analysis) an essential companion to this book as it provides a better foundation for SSA newcomers (as I am).
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