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Singular spectrum analysis for time series Nina Golyandina, Anatoly Zhigljavsky
Springer Nature - Springer Mathematics and Statistics (R0) eBooks 2013 English International Available online
View online- Format:
- Book
- Author/Creator:
- Golyandina, Nina
- Series:
- SpringerBriefs in statistics
- SpringerBriefs in statistics 2191-544X
- Language:
- English
- Subjects (All):
- Time-series analysis--Mathematical models.
- Time-series analysis.
- Spectrum analysis.
- Spectrum Analysis.
- Medical Subjects:
- Spectrum Analysis.
- Physical Description:
- 1 online resource
- Place of Publication:
- Berlin New York Springer ©2013
- Language Note:
- English
- System Details:
- text file
- Summary:
- 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
- Contents:
- Introduction Basic SSA SSA for Forecasting, Interpolation, Filtration and Estimation
- Notes:
- Includes bibliographical references
- Other Format:
- Printed edition:
- ISBN:
- 9783642349133
- 3642349137
- 9783662624364
- 3662624362
- 1299197833
- 9781299197831
- OCLC:
- 826708350
- Access Restriction:
- Restricted for use by site license
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