| 书目名称 | Singular Spectrum Analysis for Time Series |
| 编辑 | Nina Golyandina,Anatoly Zhigljavsky |
| 视频video | |
| 概述 | Presents the methodology of SSA.Shows how to use SSA both safely and with maximum effect.For professional statisticians, econometricians and specialists in any discipline.For students taking courses o |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 描述 | 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. |
| 出版日期 | Book 20131st edition |
| 关键词 | data analysis; forecasting; signal processing; singular value decomposition; time series |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-34913-3 |
| isbn_ebook | 978-3-642-34913-3Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s) 2013 |
发表于 2025-3-21 18:25:41
