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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.
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Edition | Availability |
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1
Singular Spectrum Analysis for Time Series
2020, Springer Nature
in English
3662624362 9783662624364
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2
Singular Spectrum Analysis for Time Series
2020, Springer Berlin / Heidelberg
in English
3662624354 9783662624357
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3
Singular Spectrum Analysis for Time Series
2013, Springer London, Limited
in English
3642349137 9783642349133
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4
Singular Spectrum Analysis for Time Series
Jan 18, 2013, Springer
paperback
3642349129 9783642349126
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Source title: Singular Spectrum Analysis for Time Series (SpringerBriefs in Statistics)
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