Time Series Analysis and Forecasting by Example
by Bisgaard, Sø; ren; Kulahci, MuratEdition: 1st
Format: Hardcover
Pub. Date: 2011-08-09
Publisher(s): Wiley
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Summary
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.
Author Biography
The late Soren Bisgaard, PHD, was professor of technology management at the University of Massachusetts Amherst. Throughout his esteemed career, Dr. Bisgaard made significant research contributions in the areas of experimental design, operations management, time series analysis, and Lean Six Sigma. A Fellow of the American Statistical Association and the American Society for Quality, he was also one of the cofounders of the European Network for Business and Industrial Statistics (ESB1S) in 1999. Dr. Bisgaard was awarded many honors for his achievements in the field of statistics, including the Brumbaugh Award (1988, 1996, and 2008), the Shewhart Medal (2002), the William G. Hunter Award (2002), and the George Box Award (2004). Murat Kulahci, PHD, is Associate Professor of Statistics in the Department of Informatics and Mathematical Modeling at the Technical University of Denmark. He has authored or coauthored over forty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring. Dr. Kulahci is coauthor of Introduction to Time Series Analysis and Forecasting (Wiley).
Table of Contents
| Preface | p. xi |
| Time Serirs Data: Examples and Basic Concepts | p. 1 |
| Introduction | p. 1 |
| Examples of Time Series Data | p. 1 |
| Understanding Autocorrelation | p. 10 |
| The Wold Decomposition | p. 12 |
| The Impulse Response Function | p. 14 |
| Superposition Principle | p. 15 |
| Parsimonious Models | p. 18 |
| Exercises | p. 19 |
| Visualizing Time Series Data Structures: Graphical Tools | p. 21 |
| Introduction | p. 21 |
| Graphical Analysis of Time Series | p. 22 |
| Graph Terminology | p. 23 |
| Graphical Perception | p. 24 |
| Principles of Graph Construction | p. 28 |
| Aspect Ratio | p. 30 |
| Time Series Plots | p. 34 |
| Bad Graphics | p. 38 |
| Exercises | p. 46 |
| Stationary Models | p. 47 |
| Basics of Stationary Time Series Models | p. 47 |
| Autoregressive Moving Average (ARMA) Models | p. 54 |
| Stationarity and Invertibility of ARMA Models | p. 62 |
| Checking for Stationarity using Variogram | p. 66 |
| Transformation of Data | p. 69 |
| Exercises | p. 73 |
| Nonstationary Models | p. 79 |
| Introduction | p. 79 |
| Detecting Nonstationarity | p. 79 |
| Autoregressive Integrated Moving Average (ARIMA) Models | p. 83 |
| Forecasting using ARIMA Models | p. 91 |
| Example 2: Concentration Measurements from a Chemical Process | p. 93 |
| The EWMA Forecast | p. 103 |
| Exercises | p. 104 |
| Seasonal Models | p. 111 |
| Seasonal Data | p. 111 |
| Seasonal Arima Models | p. 116 |
| Forecasting using Seasonal Arima Models | p. 124 |
| Example 2: Company X's Sales Data | p. 126 |
| Exercises | p. 152 |
| Time Series Model Selection | p. 155 |
| Introduction | p. 155 |
| Finding the "Best" Model | p. 155 |
| Example: Internet Users Data | p. 156 |
| Model Selection Criteria | p. 163 |
| Impulse Response Function to Study the Differences in Models | p. 166 |
| Comparing Impulse Response Functions for Competing Models | p. 169 |
| Arima Models as Rational Approximations | p. 170 |
| Ar Versus Arma Controversy | p. 171 |
| Final Thoughts on Model Selection | p. 173 |
| How to Compute Impulse Response Functionswith a Spreadsheet | p. 173 |
| Exercises | p. 174 |
| Additional Issues In Arima Models | p. 177 |
| Introduction | p. 177 |
| Linear Difference Equations | p. 177 |
| Eventual Forecast Function | p. 183 |
| Deterministic Trend Models | p. 187 |
| Yet Another Argument for Differencing | p. 189 |
| Constant Term in Arima Models | p. 190 |
| Cancellation of Terms in Arima Models | p. 191 |
| Stochastic Trend: Unit Root Nonstationary Processes | p. 194 |
| Overdifferencing and Underdifferencing | p. 195 |
| Missing Values in Time Series Data | p. 197 |
| Exercises | p. 201 |
| Transfer-Function Models | p. 203 |
| Introduction | p. 203 |
| Studying Input-Output Relationships | p. 203 |
| Example 1: The Box-Jenkins' Gas Furnace | p. 204 |
| Spurious Cross Correlations | p. 207 |
| Prewhitening | p. 207 |
| Identification of the Transfer Function | p. 213 |
| Modeling the Noise | p. 215 |
| The General Methodology for Transfer Function Models | p. 222 |
| Forecasting Using Transfer Function-Noise Models | p. 224 |
| Intervention Analysis | p. 238 |
| Exercises | p. 261 |
| Additional Topics | p. 263 |
| Spurious Relationships | p. 263 |
| Autocorrelation in Regression | p. 271 |
| Process Regime Changes | p. 278 |
| Analysis of Multiple Time Series | p. 296 |
| Structural Analysis of Multiple Time Series | p. 296 |
| Exercises | p. 310 |
| Datasets Used in the Examples | p. 311 |
| Temperature Readings from a Ceramic Furnace | p. 312 |
| Chemical Process Temperature Readings | p. 313 |
| Chemical Process Concentration Readings | p. 314 |
| International Airline Passengers | p. 315 |
| Company X's Sales Data | p. 316 |
| Internet Users Data | p. 317 |
| Historical Sea Level (mm) Data in Copenhagen, Denmark | p. 317 |
| Gas Furnace Data | p. 318 |
| Sales with Leading Indicator | p. 319 |
| Crest/Colgate Market Share | p. 320 |
| Simulated Process Data | p. 322 |
| Coen et al. (1969) Data | p. 323 |
| Temperature Data from a Ceramic Furnace | p. 324 |
| Temperature Readings from an Industrial Process | p. 325 |
| US Hog Series | p. 326 |
| Datasets Used in the Exercise | p. 327 |
| Beverage Amount (ml) | p. 328 |
| Pressure of the Steam Fed to a Distillation Column (bar) | p. 329 |
| Number of Paper Checks Processed in a Local Bank | p. 330 |
| Monthly Sea Levels in Los Angeles, California (mm) | p. 331 |
| Temperature Readings from a ChemicalTroeess (?C) | p. 334 |
| Daily Average Exchange Rates between US Dollar and Euro | p. 335 |
| Monthly US Unemployment Rates | p. 336 |
| Monthly Residential Electricity Sales (MWh) and Average Residential Electricity Retail Price (c/kWh) in the United States | p. 337 |
| Monthly Outstanding Consumer Credits Provided by Commercial Banks in the United States (million USD) | p. 340 |
| 100 Observations Simulated from an ARMA (1, 1) Process | p. 342 |
| Quarterly Rental Vacancy Rates in the United States | p. 343 |
| W?lfer Sunspot Numbers | p. 344 |
| Viscosity Readings from a Chemical Process | p. 345 |
| UK Midyear Population | p. 346 |
| Unemployment and GDP data for the United Kingdom | p. 347 |
| Monthly Crude Oil Production of OPEC Nations | p. 348 |
| Quarterly Dollar Sales of Marshall Field & Company ($ 1000) | p. 360 |
| Bibliography | p. 361 |
| Index | p. 365 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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