| 书目名称 | Elements of Nonlinear Time Series Analysis and Forecasting |
| 编辑 | Jan G. De Gooijer |
| 视频video | |
| 概述 | Presents a detailed, almost encyclopedic account of nonlinear time series analysis.Shows concrete applications of modern nonlinear time series analysis on a variety of empirical time series, with a li |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods..The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods..To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a |
| 出版日期 | Book 2017 |
| 关键词 | nonlinear time series; ARMA model; AR-GARCH model; time-domain linearity test; model selection; high dime |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-43252-6 |
| isbn_softcover | 978-3-319-82770-4 |
| isbn_ebook | 978-3-319-43252-6Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer International Publishing Switzerland 2017 |
发表于 2025-3-21 17:35:11
