Time Series Analysis and Forecasting by Example

ISBN-10: 0470540648
ISBN-13: 9780470540640
Edition: 2011
List price: $96.95 Buy it from $73.95 Rent it from $42.74
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Description: Times Series Analysis and Forecasting presents seemingly difficult techniques and methodologies in an insightful and application-based way. Through a hands-on and user-friendly approach, this text includes exercises, graphical techniques, examples,  More...

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Book details

List price: $96.95
Copyright year: 2011
Publisher: John Wiley & Sons, Limited
Publication date: 7/28/2011
Binding: Hardcover
Pages: 400
Size: 6.00" wide x 9.75" long x 1.00" tall
Weight: 1.738
Language: English

Times Series Analysis and Forecasting presents seemingly difficult techniques and methodologies in an insightful and application-based way. Through a hands-on and user-friendly approach, this text includes exercises, graphical techniques, examples, excel spreadsheets, and software applications on time series analysis. The reference offers step-by-step procedures and instructions. This textbook is essential for students, emphasizing intuitive learning rather than theory through modeling the data in careful interpretation and use of modern statistical graphics.

Douglas C. Montgomery, PhD, is Regents' Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery has over thirty years of academic and consulting experience and has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. He has authored or coauthored over 190 journal articles and eleven books, including Introduction to Linear Regression Analysis, Fourth Edition and Generalized Linear Models: With Applications in Engineering and the Sciences, both published by Wiley.Cheryl L. Jennings, PhD, is a Process Design Consultant with Bank of America. An active member of both the American Statistical Association and the American Society for Quality, her areas of research and professional interest include Six Sigma; modeling and analysis; and process control and improvement. Dr. Jennings earned her PhD in industrial engineering from Arizona State University.Murat Kulahci, PhD, is Associate Professor in Informatics and Mathematical Modelling at the Technical University of Denmark. He has authored or coauthored over thirty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring.Douglas C. Montgomery, PhD, is Regents' Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery has over thirty years of academic and consulting experience and has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. He has authored or coauthored over 190 journal articles and eleven books, including Introduction to Linear Regression Analysis, Fourth Edition and Generalized Linear Models: With Applications in Engineering and the Sciences, both published by Wiley.Cheryl L. Jennings, PhD, is a Process Design Consultant with Bank of America. An active member of both the American Statistical Association and the American Society for Quality, her areas of research and professional interest include Six Sigma; modeling and analysis; and process control and improvement. Dr. Jennings earned her PhD in industrial engineering from Arizona State University.Murat Kulahci, PhD, is Associate Professor in Informatics and Mathematical Modelling at the Technical University of Denmark. He has authored or coauthored over thirty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring.

Preface
Time Serirs Data: Examples and Basic Concepts
Introduction
Examples of Time Series Data
Understanding Autocorrelation
The Wold Decomposition
The Impulse Response Function
Superposition Principle
Parsimonious Models
Exercises
Visualizing Time Series Data Structures: Graphical Tools
Introduction
Graphical Analysis of Time Series
Graph Terminology
Graphical Perception
Principles of Graph Construction
Aspect Ratio
Time Series Plots
Bad Graphics
Exercises
Stationary Models
Basics of Stationary Time Series Models
Autoregressive Moving Average (ARMA) Models
Stationarity and Invertibility of ARMA Models
Checking for Stationarity using Variogram
Transformation of Data
Exercises
Nonstationary Models
Introduction
Detecting Nonstationarity
Autoregressive Integrated Moving Average (ARIMA) Models
Forecasting using ARIMA Models
Example 2: Concentration Measurements from a Chemical Process
The EWMA Forecast
Exercises
Seasonal Models
Seasonal Data
Seasonal Arima Models
Forecasting using Seasonal Arima Models
Example 2: Company X's Sales Data
Exercises
Time Series Model Selection
Introduction
Finding the "Best" Model
Example: Internet Users Data
Model Selection Criteria
Impulse Response Function to Study the Differences in Models
Comparing Impulse Response Functions for Competing Models
Arima Models as Rational Approximations
Ar Versus Arma Controversy
Final Thoughts on Model Selection
How to Compute Impulse Response Functionswith a Spreadsheet
Exercises
Additional Issues In Arima Models
Introduction
Linear Difference Equations
Eventual Forecast Function
Deterministic Trend Models
Yet Another Argument for Differencing
Constant Term in Arima Models
Cancellation of Terms in Arima Models
Stochastic Trend: Unit Root Nonstationary Processes
Overdifferencing and Underdifferencing
Missing Values in Time Series Data
Exercises
Transfer-Function Models
Introduction
Studying Input-Output Relationships
Example 1: The Box-Jenkins' Gas Furnace
Spurious Cross Correlations
Prewhitening
Identification of the Transfer Function
Modeling the Noise
The General Methodology for Transfer Function Models
Forecasting Using Transfer Function-Noise Models
Intervention Analysis
Exercises
Additional Topics
Spurious Relationships
Autocorrelation in Regression
Process Regime Changes
Analysis of Multiple Time Series
Structural Analysis of Multiple Time Series
Exercises
Datasets Used in the Examples
Temperature Readings from a Ceramic Furnace
Chemical Process Temperature Readings
Chemical Process Concentration Readings
International Airline Passengers
Company X's Sales Data
Internet Users Data
Historical Sea Level (mm) Data in Copenhagen, Denmark
Gas Furnace Data
Sales with Leading Indicator
Crest/Colgate Market Share
Simulated Process Data
Coen et al. (1969) Data
Temperature Data from a Ceramic Furnace
Temperature Readings from an Industrial Process
US Hog Series
Datasets Used in the Exercise
Beverage Amount (ml)
Pressure of the Steam Fed to a Distillation Column (bar)
Number of Paper Checks Processed in a Local Bank
Monthly Sea Levels in Los Angeles, California (mm)
Temperature Readings from a ChemicalTroeess (?C)
Daily Average Exchange Rates between US Dollar and Euro
Monthly US Unemployment Rates
Monthly Residential Electricity Sales (MWh) and Average Residential Electricity Retail Price (c/kWh) in the United States
Monthly Outstanding Consumer Credits Provided by Commercial Banks in the United States (million USD)
100 Observations Simulated from an ARMA (1, 1) Process
Quarterly Rental Vacancy Rates in the United States
W?lfer Sunspot Numbers
Viscosity Readings from a Chemical Process
UK Midyear Population
Unemployment and GDP data for the United Kingdom
Monthly Crude Oil Production of OPEC Nations
Quarterly Dollar Sales of Marshall Field & Company ($ 1000)
Bibliography
Index

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