| 书目名称 | Introduction to Time Series and Forecasting | | 编辑 | Peter J. Brockwell,Richard A. Davis | | 视频video | | | 丛书名称 | Springer Texts in Statistics | | 图书封面 |  | | 描述 | Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory mode | | 出版日期 | Textbook 19961st edition | | 关键词 | Estimator; ITSM; Likelihood; Random variable; Trend; correlation; expectation–maximization algorithm; innov | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4757-2526-1 | | isbn_ebook | 978-1-4757-2526-1Series ISSN 1431-875X Series E-ISSN 2197-4136 | | issn_series | 1431-875X | | copyright | Springer-Verlag New York 1996 |
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发表于 2025-3-21 19:38:11
