book

SAS for Forecasting Time Series, Third Edition, 3rd Edition

Name: SAS for Forecasting Time Series, Third Edition, 3rd Edition
ISBN: 9781629605449

by John C. Brocklebank, David A. Dickey, Bong Choi

March 2018

Beginner to intermediate

384 pages

12h 2m

English

SAS Institute

Read now

Unlock full access

About This Book
About The Authors
Acknowledgments
Chapter 1: Overview of Time Series
1.1 Introduction1.2 Analysis Methods and SAS/ETS Software1.2.1 Options1.2.2 How SAS/ETS Procedures Interrelate1.3 Simple Models: Regression1.3.1 Linear Regression1.3.2 Highly Regular Seasonality1.3.3 Regression with Transformed Data
Chapter 2: Simple Models: Autoregression
2.1 Introduction2.1.1 Terminology and Notation2.1.2 Statistical Background2.2 Forecasting2.2.1 PROC ARIMA for Forecasting2.2.2 Backshift Notation B for Time Series2.2.3 Yule-Walker Equations for Covariances2.3 Fitting an AR Model in PROC REG
Chapter 3: The General ARIMA Model
3.1 Introduction3.1.1 Statistical Background3.1.2 Terminology and Notation3.2 Prediction3.2.1 One-Step-Ahead Predictions3.2.2 Future Predictions3.3 Model Identification3.3.1 Stationarity and Invertibility3.3.2 Time Series Identification3.3.3 Chi-Square Check of Residuals3.3.4 Summary of Model Identification3.4 Examples and Instructions3.4.1 IDENTIFY Statement for Series 1-83.4.2 Example: Iron and Steel Export Analysis3.4.3 Estimation Methods Used in PROC ARIMA3.4.4 ESTIMATE Statement for Series 8-A3.4.5 Nonstationary Series3.4.6 Effect of Differencing on Forecasts3.4.7 Examples: Forecasting IBM Series and Silver Series3.4.8 Models for Nonstationary Data3.4.9 Differencing to Remove a Linear Trend3.4.10 Other Identification Techniques3.5 Summary of Steps for Analyzing Nonseasonal Univariate Series
Chapter 4: The ARIMA Model: Introductory Applications
4.1 Seasonal Time Series4.1.1 Introduction to Seasonal Modeling4.1.2 Model Identification4.2 Models with Explanatory Variables4.2.1 Case 1: Regression with Time Series Errors4.2.2 Case 1A: Intervention4.2.3 Case 2: Simple Transfer Functions4.2.4 Case 3: General Transfer Functions4.2.5 Case 3A: Leading Indicators4.2.6 Case 3B: Intervention4.3 Methodology and Example4.3.1 Case 1: Regression with Time Series Errors4.3.2 Case 2: Simple Transfer Functions4.3.3 Case 3: General Transfer Functions4.3.4 Case 3B: Intervention4.4 Further Example4.4.1 North Carolina Retail Sales4.4.2 Construction Series Revisited4.4.3 Milk Scare (Intervention)4.4.4 Terrorist Attack
Chapter 5: The ARIMA Model: Special Applications
5.1 Regression with Time Series Errors and Unequal Variances5.1.1 Autoregressive Errors5.1.2 Example: Energy Demand at a University5.1.3 Unequal Variances5.1.4 ARCH, GARCH, and IGARCH for Unequal Variances5.2 Cointegration5.2.1 Cointegration and Eigenvalues5.2.2 Impulse Response Function5.2.3 Roots in Higher-Order Models5.2.4 Cointegration and Unit Roots5.2.5 An Illustrative Example5.2.6 Estimation of the Cointegrating Vector5.2.7 Intercepts and More Lags5.2.8 PROC VARMAX5.2.9 Interpretation of the Estimates5.2.10 Diagnostics and Forecasts
Chapter 6: Exponential Smoothing
6.1 Single Exponential Smoothing6.1.1 The Smoothing Idea6.1.2 Forecasting with Single Exponential Smoothing6.1.3 Alternative Representations6.1.4 Atlantic Ocean Tides: An Example6.1.5 Improving the Tide Forecasts6.2 Exponential Smoothing for Trending Data6.2.1 Linear and Double Exponential Smoothing6.2.2 Properties of the Forecasts6.2.3 A Generated Multi-Series Example6.2.4 Real Data Examples6.2.5 Boundary Values in Linear Exponential Smoothing6.2.6 Damped Trend Exponential Smoothing6.2.7 Diagnostic Plots6.2.8 Sums of Forecasts6.3 Smoothing Seasonal Data6.3.1 Seasonal Exponential Smoothing6.3.2 Winters Method6.4.1 Validation6.4.2 Choosing a Model Visually6.4.3 Choosing a Model Numerically6.5 Advantages of Exponential Smoothing6.6 How the Smoothing Equations Lead to ARIMA in the Linear Case
Chapter 7: Unobserved Components and State Space Models
7.1 Nonseasonal Unobserved Components Models7.1.1 The Nature of Unobserved Components Models7.1.2 A Look at the PROC UCM Output7.1.3 A Note on Unit Roots in Practice7.1.4 The Basic Structural Model Related to ARIMA Structures7.1.5 A Follow-Up on the Example7.2 Diffuse Likelihood and Kalman Filter: Overview and a Simple Case7.2.1 Diffuse Likelihood in a Simple Model7.2.2 Definition of a Diffuse Likelihood7.2.3 A Numerical Example7.3 Seasonality in Unobserved Components Models7.3.1 Description of Seasonal Recursions7.3.2 Tourism Example with Regular Seasonality7.3.3 Decomposition7.3.4 Another Seasonal Model: Sine and Cosine Terms7.3.5 Example with Trigonometric Components7.3.6 The Seasonal Component Made Local and Damped7.4 A Brief Introduction to the SSM Procedure7.4.1 Brief Overview7.4.2 Simple Examples7.4.3 Extensions of the AR(1) Model7.4.4 Accommodation for Curvature7.4.5 Models with Several Lags7.4.6 Bivariate Examples7.4.7 The Start-up Problem Revisited7.4.8 Example and More Details on the State Space Approach

Chapter 8: Adjustment for Seasonality with PROC X13
8.1 Introduction8.2 The X-11 Method8.2.1 Moving Averages8.2.2 Outline of the X-11 Method8.2.3 Basic Seasonal Adjustment Using the X-11 Method8.2.4 Tests for Seasonality8.3 regARIMA Models and TRAMO8.3.1 regARIMA Models8.3.2 Automatic Selection of ARIMA Orders8.4 Data Examples8.4.1 Airline Passengers Revisited8.4.3 Employment in the United States
Chapter 9: SAS Forecast Studio
9.1 Introduction9.2 Creating a Project9.3 Overview of Available Modes9.4 Project Settings9.4.1 Model Generation9.4.2 Goodness of Fit and Honest Assessment9.4.2 Transformation and Outlier Detection9.5 Creating Custom Events9.6 Hierarchical Time Series and Reconciliation
Chapter 10: Spectral Analysis
10.1 Introduction10.2 Example: Plant Enzyme Activity10.3 PROC SPECTRA10.4 Tests for White Noise10.5 Harmonic Frequencies10.6 Extremely Fast Fluctuations and Aliasing10.7 The Spectral Density10.8 Some Mathematical Detail (Optional Reading)10.9 Estimation of the Spectrum: The Smoothed Periodogram10.10 Cross-Spectral Analysis10.10.1 Interpretation of Cross-Spectral Quantities10.10.2 Interpretation of Cross-Amplitude and Phase Spectra10.10.3 PROC SPECTRA Statements10.10.4 Cross-Spectral Analysis of the Neuse River Data10.10.5 Details on Gain, Phase, and Pure Delay
References
Index

Overview

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
Seasonal adjustment
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.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Time Series Analysis: Forecasting and Control, Fourth Edition

Publisher Resources

ISBN: 9781629605449

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills