SAS for Forecasting Time Series, Second Edition 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


SAS for Forecasting Time Series, Second Edition

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John C. Brocklebank Ph.D. 作者
SAS Institute
譯者
2003-4-18 出版日期
424 頁數
GBP 43.71 價格
Paperback
叢書系列
9781590471821 圖書編碼

SAS for Forecasting Time Series, Second Edition 在線電子書 圖書標籤: forcasting  SAS   


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SAS for Forecasting Time Series, Second Edition 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

SAS for Forecasting Time Series, Second Edition 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

SAS for Forecasting Time Series, Second Edition 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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SAS for Forecasting Time Series, Second Edition 在線電子書 著者簡介


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SAS for Forecasting Time Series, Second Edition 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

SAS for Forecasting Time Series, Second Edition 在線電子書 圖書描述

In this second edition of the indispensable SAS for Forecasting Time Series, 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 how 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.

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