| Description |
xiii, 366 pages : illustrations ; 25 cm |
| Series |
Wiley series in probability and statistics |
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Wiley series in probability and statistics.
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| Contents |
Contents note continued: 4.3.Autoregressive Integrated Moving Average (ARIMA) Models -- 4.4.Forecasting using ARIMA Models -- 4.5.Example 2: Concentration Measurements from a Chemical Process -- 4.6.The EWMA Forecast -- Exercises -- 5.Seasonal Models -- 5.1.Seasonal Data -- 5.2.Seasonal ARIMA Models -- 5.3.Forecasting using Seasonal ARIMA Models -- 5.4.Example 2: Company X's Sales Data -- Exercises -- 6.Time Series Model Selection -- 6.1.Introduction -- 6.2.Finding the "BEST" Model -- 6.3.Example: Internet Users Data -- 6.4.Model Selection Criteria -- 6.5.Impulse Response Function to Study the Differences in Models -- 6.6.Comparing Impulse Response Functions for Competing Models -- 6.7.ARIMA Models as Rational Approximations -- 6.8.AR Versus Arma Controversy -- 6.9.Final Thoughts on Model Selection -- Appendix 6.1 How to Compute Impulse Response Functions with a Spreadsheet -- Exercises -- 7.Additional Issues In Arima Models -- 7.1.Introduction -- |
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Contents note continued: 7.2.Linear Difference Equations -- 7.3.Eventual Forecast Function -- 7.4.Deterministic Trend Models -- 7.5.Yet Another Argument for Differencing -- 7.6.Constant Term in ARIMA Models -- 7.7.Cancellation of Terms in ARIMA Models -- 7.8.Stochastic Trend: Unit Root Nonstationary Processes -- 7.9.Overdifferencing and Underdifferencing -- 7.10.Missing Values in Time Series Data -- Exercises -- 8.Transfer Function Models -- 8.1.Introduction -- 8.2.Studying Input-Output Relationships -- 8.3.Example 1: The Box-Jenkins' Gas Furnace -- 8.4.Spurious Cross Correlations -- 8.5.Prewhitening -- 8.6.Identification of the Transfer Function -- 8.7.Modeling the Noise -- 8.8.The General Methodology for Transfer Function Models -- 8.9.Forecasting Using Transfer Function-Noise Models -- 8.10.Intervention Analysis -- Exercises -- 9.Additional Topics -- 9.1.Spurious Relationships -- 9.2.Autocorrelation in Regression -- 9.3.Process Regime Changes -- |
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Contents note continued: 9.4.Analysis of Multiple Time Series -- 9.5.Structural Analysis of Multiple Time Series -- Exercises -- Appendix A DATASETS USED IN THE EXAMPLES -- Table A.1 Temperature Readings from a Ceramic Furnace -- Table A.2 Chemical Process Temperature Readings -- Table A.3 Chemical Process Concentration Readings -- Table A.4 International Airline Passengers -- Table A.5 Company X's Sales Data -- Table A.6 Internet Users Data -- Table A.7 Historical Sea Level (mm) Data in Copenhagen, Denmark -- Table A.8 Gas Furnace Data -- Table A.9 Sales with Leading Indicator -- Table A.10 Crest/Colgate Market Share -- Table A.11 Simulated Process Data -- Table A.12 Coen et al. (1969) Data -- Table A.13 Temperature Data from a Ceramic Furnace -- Table A.14 Temperature Readings from an Industrial Process -- Table A.15 US Hog Series -- Appendix B DATASETS USED IN THE EXERCISES -- Table B.1 Beverage Amount (ml) -- |
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Contents note continued: Table B.16 Monthly Crude Oil Production of OPEC Nations -- Table B.17 Quarterly Dollar Sales of Marshall Field & Company ($1000) |
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Contents note continued: Table B.2 Pressure of the Steam Fed to a Distillation Column (bar) -- Table B.3 Number of Paper Checks Processed in a Local Bank -- Table B.4 Monthly Sea Levels in Los Angeles, California (mm) -- Table B.5 Temperature Readings from a Chemical Process (°C) -- Table B.6 Daily Average Exchange Rates between US Dollar and Euro -- Table B.7 Monthly US Unemployment Rates -- Table B.8 Monthly Residential Electricity Sales (MWh) and Average Residential Electricity Retail Price (c/kWh) in the United States -- Table B.9 Monthly Outstanding Consumer Credits Provided by Commercial Banks in the United States (million USD) -- Table B.10 100 Observations Simulated from an ARMA (1, 1) Process -- Table B.11 Quarterly Rental Vacancy Rates in the United States -- Table B.12 Wolfer Sunspot Numbers -- Table B.13 Viscosity Readings from a Chemical Process -- Table B.14 UK Midyear Population -- Table B.15 Unemployment and GDP data for the United Kingdom -- |
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Machine generated contents note: 1.Time Series Data: Examples And Basic Concepts -- 1.1.Introduction -- 1.2.Examples of Time Series Data -- 1.3.Understanding Autocorrelation -- 1.4.The Wold Decomposition -- 1.5.The Impulse Response Function -- 1.6.Superposition Principle -- 1.7.Parsimonious Models -- Exercises -- 2.Visualizing Time Series Data Structures: Graphical Tools -- 2.1.Introduction -- 2.2.Graphical Analysis of Time Series -- 2.3.Graph Terminology -- 2.4.Graphical Perception -- 2.5.Principles of Graph Construction -- 2.6.Aspect Ratio -- 2.7.Time Series Plots -- 2.8.Bad Graphics -- Exercises -- 3.Stationary Models -- 3.1.Basics of Stationary Time Series Models -- 3.2.Autoregressive Moving Average (ARMA) Models -- 3.3.Stationarity and Invertibility of ARMA Models -- 3.4.Checking for Stationarity using Variogram -- 3.5.Transformation of Data -- Exercises -- 4.Nonstationary Models -- 4.1.Introduction -- 4.2.Detecting Nonstationarity -- |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Forecasting.
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Time-series analysis.
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| Author |
Kulahci, Murat.
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| LC no. |
2010048281 |
| ISBN |
0470540648 (hbk.) |
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9780470540640 (hbk.) |
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(oBook) |
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(ePub) |
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