{"product_id":"sas-for-forecasting-time-series-isbn-9780471395669","title":"SAS for Forecasting Time Series","description":"Easy-to-read and comprehensive, this book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. The interrelationship of SAS\/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analysed and the reults desired. Other topics covered include detecting sinusoidal components in time series models and performing bivariate corr-spectral analysis and comparing the results with the standard transfer function methodology. The authors? unique approach to integrating students in a variety of disciplines and industries. Emphasis is on correct interpretation of output to draw meaningful conclusions. The volume, co-pubished by SAS and JWS, features both theory and practicality, and accompanies a soon-to-be extensive library of SAS hands-on manuals in a multitude of statistical areas. The book can be used with a number of hardware-specific computing machines including CMS, Mac, MVS, Opem VMS Alpha, Opmen VMS VAX, OS\/390, OS\/2, UNIX, and Windows. Chapter 1- Overview of Time Series.\u003cbr\u003e \u003cbr\u003e Chapter 2- Simple Models: Autoregression.\u003cbr\u003e \u003cbr\u003e Chapter 3- The General ARIMA Model.\u003cbr\u003e \u003cbr\u003e Chapter 4- The ARIMA Model: Introductory Applications.\u003cbr\u003e \u003cbr\u003e Chapter 5- The ARIMA Model: Special Applications.\u003cbr\u003e \u003cbr\u003e Chapter 6- State Space Modeling.\u003cbr\u003e \u003cbr\u003e Chapter 7- Spectral Analysis. \"The new material and the update of the excellent 1E, now 17 years in the past, certainly make the 2E a necessary purchase for any user of SAS time series modeling methods.\"\u003cbr\u003e Technometrics\u003cbr\u003e Vol. 46, No. 1, February 2004\u003cbr\u003e John C. Brocklebank is Mgr. of Stats. Training at the SAS Institute. David A. Dickey is Associate Professor of Statistics at North Carolina State University.  \u003cb\u003eIn this second edition of the indispensable SAS\u003csup\u003e®\u003c\/sup\u003e for Forecasting Time Series,\u003c\/b\u003e 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, STRATESPACE, and VARMAX. They demonstrate the interrelationship of SAS\/ETS\u003csup\u003e®\u003c\/sup\u003e 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 and vector ARMA processes using the STATE-SPACE 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.  \u003cp\u003e\u003cb\u003eNew and updated examples in the second edition include\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cdiv\u003eRetail sales with seasonality\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eARCH models for stock prices with changing volatility\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eVector autoregression and cointegration models\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eIntervention analysis for product recall data\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eExpanded discussion of unit root tests and nonstationarity\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eExpanded discussion of frequency domain analysis and cycles in data\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eData mining and forecasting with examples using SAS IntelliVisor\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eUsing the HPF procedure to automatically generate forecasts for several time series in one step\u003c\/div\u003e \u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley-SAS","offers":[{"title":"Default Title","offer_id":47989987999973,"sku":"NP9780471395669","price":136.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780471395669.jpg?v=1761786117","url":"https:\/\/k12savings.com\/es\/products\/sas-for-forecasting-time-series-isbn-9780471395669","provider":"K12savings","version":"1.0","type":"link"}