| Preface |
| Time Serirs Data: Examples and Basic Concepts / 1： |
| Introduction / 1.1： |
| Examples of Time Series Data / 1.2： |
| Understanding Autocorrelation / 1.3： |
| The Wold Decomposition / 1.4： |
| The Impulse Response Function / 1.5： |
| Superposition Principle / 1.6： |
| Parsimonious Models / 1.7： |
| Exercises |
| Visualizing Time Series Data Structures: Graphical Tools / 2： |
| Graphical Analysis of Time Series / 2.1： |
| Graph Terminology / 2.3： |
| Graphical Perception / 2.4： |
| Principles of Graph Construction / 2.5： |
| Aspect Ratio / 2.6： |
| Time Series Plots / 2.7： |
| Bad Graphics / 2.8： |
| Stationary Models / 3： |
| Basics of Stationary Time Series Models / 3.1： |
| Autoregressive Moving Average (ARMA) Models |
| Stationarity and Invertibility of ARMA Models / 3.3： |
| Checking for Stationarity using Variogram / 3.4： |
| Transformation of Data / 3.5： |
| Nonstationary Models / 4： |
| Detecting Nonstationarity / 4.1： |
| Autoregressive Integrated Moving Average (ARIMA) Models / 4.3： |
| Forecasting using ARIMA Models / 4.4： |
| Example 2: Concentration Measurements from a Chemical Process / 4.5： |
| The EWMA Forecast / 4.6： |
| Seasonal Models / 5： |
| Seasonal Data / 5.1： |
| Seasonal Arima Models / 5.2： |
| Forecasting using Seasonal Arima Models / 5.3： |
| Example 2: Company X's Sales Data / 5.4： |
| Time Series Model Selection / 6： |
| Finding the "Best" Model / 6.1： |
| Example: Internet Users Data / 6.3： |
| Model Selection Criteria / 6.4： |
| Impulse Response Function to Study the Differences in Models / 6.5： |
| Comparing Impulse Response Functions for Competing Models / 6.6： |
| Arima Models as Rational Approximations / 6.7： |
| Ar Versus Arma Controversy / 6.8： |
| Final Thoughts on Model Selection / 6.9： |
| How to Compute Impulse Response Functionswith a Spreadsheet / Appendix 6.1： |
| Additional Issues In Arima Models / 7： |
| Linear Difference Equations / 7.1： |
| Eventual Forecast Function / 7.3： |
| Deterministic Trend Models / 7.4： |
| Yet Another Argument for Differencing / 7.5： |
| Constant Term in Arima Models / 7.6： |
| Cancellation of Terms in Arima Models / 7.7： |
| Stochastic Trend: Unit Root Nonstationary Processes / 7.8： |
| Overdifferencing and Underdifferencing / 7.9： |
| Missing Values in Time Series Data / 7.10： |
| Transfer-Function Models / 8： |
| Studying Input-Output Relationships / 8.1： |
| Example 1: The Box-Jenkins' Gas Furnace / 8.3： |
| Spurious Cross Correlations / 8.4： |
| Prewhitening / 8.5： |
| Identification of the Transfer Function / 8.6： |
| Modeling the Noise / 8.7： |
| The General Methodology for Transfer Function Models / 8.8： |
| Forecasting Using Transfer Function-Noise Models / 8.9： |
| Intervention Analysis / 8.10： |
| Additional Topics / 9： |
| Spurious Relationships / 9.1： |
| Autocorrelation in Regression / 9.2： |
| Process Regime Changes / 9.3： |
| Analysis of Multiple Time Series / 9.4： |
| Structural Analysis of Multiple Time Series / 9.5： |
| Datasets Used in the Examples / Appendix A： |
| Temperature Readings from a Ceramic Furnace / Table A.1： |
| Chemical Process Temperature Readings / Table A.2： |
| Chemical Process Concentration Readings / Table A.3： |
| International Airline Passengers / Table A.4： |
| Company X's Sales Data / Table A.5： |
| Internet Users Data / Table A.6： |
| Historical Sea Level (mm) Data in Copenhagen, Denmark / Table A.7： |
| Gas Furnace Data / Table A.8： |
| Sales with Leading Indicator / Table A.9： |
| Crest/Colgate Market Share / Table A.10： |
| Simulated Process Data / Table A.11： |
| Coen et al. (1969) Data / Table A.12： |
| Temperature Data from a Ceramic Furnace / Table A.13： |
| Temperature Readings from an Industrial Process / Table A.14： |
| US Hog Series / Table A.15： |
| Datasets Used in the Exercise / Appendix B： |
| Beverage Amount (ml) / Table B.l： |
| Pressure of the Steam Fed to a Distillation Column (bar) / Table B.2： |
| Number of Paper Checks Processed in a Local Bank / Table B.3： |
| Monthly Sea Levels in Los Angeles, California (mm) / Table B.4： |
| Temperature Readings from a ChemicalTroeess (?C) / Table B.5： |
| Daily Average Exchange Rates between US Dollar and Euro / Table B.6： |
| Monthly US Unemployment Rates / Table B.7： |
| Monthly Residential Electricity Sales (MWh) and Average Residential Electricity Retail Price (c/kWh) in the United States / Table B.8： |
| Monthly Outstanding Consumer Credits Provided by Commercial Banks in the United States (million USD) / Table B.9： |
| 100 Observations Simulated from an ARMA (1, 1) Process / Table B.10： |
| Quarterly Rental Vacancy Rates in the United States / Table B.11： |
| W?lfer Sunspot Numbers / Table B.12： |
| Viscosity Readings from a Chemical Process / Table B.13： |
| UK Midyear Population / Table B.14： |
| Unemployment and GDP data for the United Kingdom / Table B.15： |
| Monthly Crude Oil Production of OPEC Nations / Table B.16： |
| Quarterly Dollar Sales of Marshall Field & Company ($ 1000) / Table B.17： |
| Bibliography |
| Index |
| Preface |
| Time Serirs Data: Examples and Basic Concepts / 1： |
| Introduction / 1.1： |
図書
