Time Series Analysis and Forecasting by Example
Published on 24. August 2011
400 pages
978-1-118-05695-0 (ISBN)
Description
An intuition-based approach enables you to master time seriesanalysis with ease
Time Series Analysis and Forecasting by Example providesthe fundamental techniques in time series analysis using variousexamples. By introducing necessary theory through examples thatshowcase the discussed topics, the authors successfully helpreaders develop an intuitive understanding of seemingly complicatedtime series models and their implications.
The book presents methodologies for time series analysis in asimplified, example-based approach. Using graphics, the authorsdiscuss each presented example in detail and explain therelevant theory while also focusing on the interpretationof results in data analysis. Following a discussion of whyautocorrelation is often observed when data is collected in time,subsequent chapters explore related topics, including:
* Graphical tools in time seriesanalysis
* Procedures for developing stationary, non-stationary, andseasonal models
* How to choose the best time series model
* Constant term and cancellation of terms in ARIMA models
* Forecasting using transfer function-noise models
The final chapter is dedicated to key topics such as spuriousrelationships, autocorrelation in regression, and multiple timeseries. Throughout the book, real-world examples illustratestep-by-step procedures and instructions using statistical softwarepackages such as SAS®, JMP, Minitab, SCA, and R. A related Website features PowerPoint slides to accompany each chapter as wellas the book's data sets.
With its extensive use of graphics and examples to explain keyconcepts, Time Series Analysis and Forecasting by Example isan excellent book for courses on time series analysis at theupper-undergraduate and graduate levels. it also serves as avaluable resource for practitioners and researchers who carry outdata and time series analysis in the fields of engineering,business, and economics.
Reviews / Votes
"It is a suitable text for courses on time series analysisat the (upper) undergraduate and graduate level. It can also serveas a guide for practitioners and researchers who carry out timeseries analysis in engineering, business andeconomics." (Zentralblatt MATH, 2012)"Time Series Analysis and Forecasting by Example is wellrecommended as a great introductory book for students transitioningfrom general statistics to time series as well as a good sourcebook for intermediate level time series model builders." (BookPleasures, 2012) "They set out to provide an introduction that is easy tounderstand and use, and that draws heavily from examples todemonstrate the principles and techniques." (Book News, 1 October2011)More details
Other editions
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Søren Bisgaard | Murat Kulahci
Time Series Analysis and Forecasting by Example
Book
07/2011
Wiley
€132.00
Shipment within 10-20 days
S. Bisgaard
Time Series Analysis and Forecasting By Example
Software
05/2011
Wiley
€245.84
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Persons
The late Søren Bisgaard, PhD, was professor oftechnology management at the University of Massachusetts Amherst.Throughout his esteemed career, Dr. Bisgaard made significantresearch contributions in the areas of experimental design,operations management, time series analysis, and Lean Six Sigma. AFellow of the American Statistical Association and the AmericanSociety for Quality, he was also one of the cofounders of theEuropean Network for Business and Industrial Statistics (ESBIS) in1999. Dr. Bisgaard was awarded many honors for his achievements inthe 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 Statisticsin the Department of Informatics and Mathematical Modeling at theTechnical University of Denmark. He has authored or coauthored overforty journal articles in the areas of time series analysis, designof experiments, and statistical process control and monitoring. Dr.Kulahci is coauthor of Introduction to Time Series Analysis andForecasting (Wiley).
Content
Preface xi
1. Time Series Data: Examples and Basic Concepts 1
1.1 Introduction 1
1.2 Examples of Time Series Data 1
1.3 Understanding Autocorrelation 10
1.4 The World Decomposition 12
1.5 The Impulse Response Function 14
1.6 Superposition Principle 15
1.7 Parsimonious Models 18
Exercises 19
2. Visualizing Time Series Data Structures: Graphical Tools21
2.1 Introduction 21
2.2 Graphical Analysis of Time Series 22
2.3 Graph Terminology 23
2.4 Graphical Perception 24
2.5 Principles of Graph Construction 28
2.6 Aspect Ratio 30
2.7 Time Series Plots 34
2.8 Bad Graphics 38
Exercises 46
3. Stationary Models 47
3.1 Basics of Stationary Time Series Models 47
3.2 Autoregressive Moving Average (ARMA) Models 54
3.3 Stationary and Invertibility of ARMA Models 62
3.4 Checking for Stationary using Variogram 66
3.5 Transformation of Data 69
Exercises 73
4. Nonstationary Models 79
4.1 Introduction 79
4.2 Detecting Nonstationarity 79
4.3 Antoregressive Integrated Moving Average (ARIMA) Models83
4.4 Forecasting using ARIMA Models 91
4.5 Example 2: Concentration Measurements from a ChemicalProcess 93
4.6 The EWMA Forecast 103
Exercises 104
5. Seasonal Models 111
5.1 Seasonal Data 111
5.2 Seasonal ARIMA Models 116
5.3 Forecasting using Seasonal ARIMA Models 124
5.4 Example 2: Company X's Sales Data 126
Exercises 152
6. Time Series Model Selection 155
6.1 Introduction 155
6.2 Finding the "BEST" Model 155
6.3 Example: Internet Users Data 156
6.4 Model Selection Criteria 163
6.5 Impulse Response Function to Study the Differences in Models166
6.6 Comparing Impulse Response Functions for Competing Models169
6.7 ARIMA Models as Rational Approximations 170
6.8 AR Versus Arma Controversy 171
6.9 Final Thoughts on Model Selection 173
Appendix 6.1: How to Compute Impulse Response Functions with aSpreadsheet 173
Exercises 174
7. Additional Issues in ARIMA Models 177
7.1 Introduction 177
7.2 Linear Difference Equations 177
7.3 Eventual Forecast Function 183
7.4 Deterministic Trend Models 187
7.5 Yet Another Argument for Differencing 189
7.6 Constant Term in ARIMA Models 190
7.7 Cancellation of Terms in ARIMA Models 191
7.8 Stochastic Trend: Unit Root Nonstationary Processes 194
7.9 Overdifferencing and Underdifferencing 195
7.10 Missing Values in Time Series Data 197
Exercises 201
8. Transfer Function Models 203
8.1 Introduction 203
8.2 Studying Input-Output Relationships 203
8.3 Example 1: The Box-Jenkins' Gas Furnace 204
8.4 Spurious Cross Correlations 207
8.5 Prewhitening 207
8.6 Identification of the Transfer Function 213
8.7 Modeling the Noise 215
8.8 The General Methodology for Transfer Function Models 222
8.9 Forecasting Using Transfer Function-Noise Models 224
8.10 Intervention Analysis 238
Exercises 261
9. Addition Topics 263
9.1 Spurious Relationships 263
9.2 Autocorrelation in Regression 271
9.3 Process Regime Changes 278
9.4 Analysis of Multiple Time Series 285
9.5 Structural Analysis of Multiple Time Series 296
Exercises 310
Appendix A. Datasets Used in the Examples 311
Appendix B. Datasets Used in the Exercises 327
Bibliography 361
Index 365
