| 書(shū)目名稱(chēng) | Singular Spectrum Analysis for Time Series | | 編輯 | Nina Golyandina,Anatoly Zhigljavsky | | 視頻video | http://file.papertrans.cn/868/867912/867912.mp4 | | 概述 | Presents the methodology of Singular Spectrum Analysis (SSA).Describes Multivariate Singular Spectrum Analysis (MSSA) and SSA for image processing (2D-SSA).Illustrated with examples and case studies | | 叢書(shū)名稱(chēng) | SpringerBriefs in Statistics | | 圖書(shū)封面 |  | | 描述 | 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, sig | | 出版日期 | Book 2020Latest edition | | 關(guān)鍵詞 | forecasting; signal processing; singular value decomposition; time series; signal extraction ; ; Multivari | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-662-62436-4 | | isbn_softcover | 978-3-662-62435-7 | | isbn_ebook | 978-3-662-62436-4Series ISSN 2191-544X Series E-ISSN 2191-5458 | | issn_series | 2191-544X | | copyright | The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2020 |
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