[下载][PDF]SAS for Forecasting Time Series, Second Edition

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<p>SAS for Forecasting Time Series, Second Edition</p><p>【书名】&nbsp;SAS for Forecasting Time Series, Second Edition <br/>【作者】John C.; Ph.D. Brocklebank; David A. Dickey<br/>【出版社】SAS Publishing<br/>【版本】第二版（Second Edition）<br/>【出版日期】2003<br/>【文件格式】PDF<br/>【文件大小】18M<br/>【页数】418<br/>【ISBN出版号】1590471822<br/>【资料类别】统计学<br/>【市面定价】N/A<br/>【扫描版还是影印版】清晰，非扫描<br/>【是否缺页】否<br/>【关键词】SAS，&nbsp;Time Series,&nbsp;&nbsp; Forcasting， <br/>【内容简介】In this second edition of the indispensable <i>SAS for Forecasting Time Series</i>, Brocklebank and Dickey show you how SAS performs univariate and multivariate time series analysis. Taking a tutorial approach, the authors focus on the procedures that most effectively bring results: the advanced procedures ARIMA, SPECTRA, STATESPACE, and VARMAX. They demonstrate the interrelationship of SAS/ETS procedures with a discussion of how the choice of a procedure depends on the data to be analyzed and the results desired. With this book, you will learn to model and forecast simple autoregressive (AR) processes using PROC ARIMA, and you will learn to fit autoregressive and vector ARMA processes using the STATESPACE and VARMAX procedures. Other topics covered include detecting sinusoidal components in time series models, performing bivariate cross-spectral analysis, and comparing these frequency-based results with the time domain transfer function methodology. New and updated examples in the second edition include retail sales with seasonality, ARCH models for stock prices with changing volatility, vector autoregression and cointegration models, intervention analysis for product recall data, expanded discussion of unit root tests and nonstationarity, and expanded discussion of frequency domain analysis and cycles in data. <br/><br/></p> 263258.rar (3 MB)

2008-11-4 09:49:00 上传

[下载][PDF]SAS for Forecasting Time Series, Second Edition

<br/> 263261.rar (2.28 MB)

2008-11-4 09:54:00 上传

[下载][PDF]SAS for Forecasting Time Series, Second Edition

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关键词：Forecasting Time Series Forecast forecas Casting PDF Edition SAS Series Forecasting

<p>Chapter 1 Overview of Time Series............................................. 1<br/>Chapter 2 Simple Models: Autoregression ..................................... 27<br/>Chapter 3 The General ARIMA Model ........................................... 49<br/>Chapter 4 The ARIMA Model: Introductory Applications ....................... 143<br/>Chapter 5 The ARIMA Model: Special Application.............................. 239<br/>Chapter 6 State Space Modeling ............................................. 283<br/>Chapter 7 Spectral Analysis .................................................323<br/>Chapter 8 Data Mining and Forecasting ...................................... 359<br/></p><p></p><p></p>

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