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## Book Description

To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications.

Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures.

Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods:

• ARIMA models
• Vector autoregressive models
• Exponential smoothing models
• Unobserved component and state-space models
• Spectral analysis

Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition:

• The ARIMA procedure
• The AUTOREG procedure
• The VARMAX procedure
• The ESM procedure
• The UCM and SSM procedures
• The X13 procedure
• The SPECTRA procedure
• SAS Forecast Studio

Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs.

This book is part of the SAS Press program.

3. Acknowledgments
4. Chapter 1: Overview of Time Series
1. 1.1 Introduction
2. 1.2 Analysis Methods and SAS/ETS Software
3. 1.3 Simple Models: Regression
5. Chapter 2: Simple Models: Autoregression
1. 2.1 Introduction
2. 2.2 Forecasting
3. 2.3 Fitting an AR Model in PROC REG
6. Chapter 3: The General ARIMA Model
1. 3.1 Introduction
2. 3.2 Prediction
3. 3.3 Model Identification
4. 3.4 Examples and Instructions
5. 3.5 Summary of Steps for Analyzing Nonseasonal Univariate Series
7. Chapter 4: The ARIMA Model: Introductory Applications
1. 4.1 Seasonal Time Series
2. 4.2 Models with Explanatory Variables
3. 4.3 Methodology and Example
4. 4.4 Further Example
8. Chapter 5: The ARIMA Model: Special Applications
1. 5.1 Regression with Time Series Errors and Unequal Variances
2. 5.2 Cointegration
9. Chapter 6: Exponential Smoothing
1. 6.1 Single Exponential Smoothing
2. 6.2 Exponential Smoothing for Trending Data
3. 6.3 Smoothing Seasonal Data
4. 6.5 Advantages of Exponential Smoothing
5. 6.6 How the Smoothing Equations Lead to ARIMA in the Linear Case
10. Chapter 7: Unobserved Components and State Space Models
1. 7.1 Nonseasonal Unobserved Components Models
2. 7.2 Diffuse Likelihood and Kalman Filter: Overview and a Simple Case
3. 7.3 Seasonality in Unobserved Components Models
4. 7.4 A Brief Introduction to the SSM Procedure
11. Chapter 8: Adjustment for Seasonality with PROC X13
1. 8.1 Introduction
2. 8.2 The X-11 Method
3. 8.3 regARIMA Models and TRAMO
4. 8.4 Data Examples
12. Chapter 9: SAS Forecast Studio
13. Chapter 10: Spectral Analysis
1. 10.1 Introduction
2. 10.2 Example: Plant Enzyme Activity
3. 10.3 PROC SPECTRA
4. 10.4 Tests for White Noise
5. 10.5 Harmonic Frequencies
6. 10.6 Extremely Fast Fluctuations and Aliasing
7. 10.7 The Spectral Density
8. 10.8 Some Mathematical Detail (Optional Reading)
9. 10.9 Estimation of the Spectrum: The Smoothed Periodogram
10. 10.10 Cross-Spectral Analysis
14. References
15. Index