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Preface | |
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Acknowledgments | |
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Overview of Time Series | |
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Introduction | |
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Analysis Methods and SAS/ETS Software | |
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Options | |
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How SAS/ETS Software Procedures Interrelate | |
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Simple Models: Regression | |
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Linear Regression | |
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Highly Regular Seasonality | |
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Regression with Transformed Data | |
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Simple Models: Autoregression | |
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Introduction | |
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Terminology and Notation | |
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Statistical Background | |
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Forecasting | |
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Forecasting with PROC ARIMA | |
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Backshift Notation B for Time Series | |
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Yule-Walker Equations for Covariances | |
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Fitting an AR Model in PROC REG | |
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The General ARIMA Model | |
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Introduction | |
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Statistical Background | |
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Terminology and Notation | |
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Prediction | |
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One-Step-Ahead Predictions | |
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Future Predictions | |
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Model Identification | |
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Stationarity and Invertibility | |
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Time Series Identification | |
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Chi-Square Check of Residuals | |
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Summary of Model Identification | |
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Examples and Instructions | |
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IDENTIFY Statement for Series 1-8 | |
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Example: Iron and Steel Export Analysis | |
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Estimation Methods Used in PROC ARIMA | |
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ESTIMATE Statement for Series 8 | |
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Nonstationary Series | |
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Effect of Differencing on Forecasts | |
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Examples: Forecasting IBM Series and Silver Series | |
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Models for Nonstationary Data | |
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Differencing to Remove a Linear Trend | |
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Other Identification Techniques | |
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Summary | |
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The ARIMA Model: Introductory Applications | |
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Seasonal Time Series | |
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Introduction to Seasonal Modeling | |
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Model Identification | |
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Models with Explanatory Variables | |
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Case 1: Regression with Time Series Errors | |
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Case 1A: Intervention | |
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Case 2: Simple Transfer Function | |
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Case 3: General Transfer Function | |
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Case 3A: Leading Indicators | |
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Case 3B: Intervention | |
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Methodology and Example | |
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Case 1: Regression with Time Series Errors | |
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Case 2: Simple Transfer Functions | |
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Case 3: General Transfer Functions | |
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Case 3B: Intervention | |
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Further Examples | |
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North Carolina Retail Sales | |
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Construction Series Revisited | |
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Milk Scare (Intervention) | |
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Terrorist Attack | |
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The ARIMA Model: Special Applications | |
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Regression with Time Series Errors and Unequal Variances | |
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Autoregressive Errors | |
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Example: Energy Demand at a University | |
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Unequal Variances | |
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ARCH, GARCH, and IGARCH for Unequal Variances | |
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Cointegration | |
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Introduction | |
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Cointegration and Eigenvalues | |
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Impulse Response Function | |
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Roots in Higher-Order Models | |
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Cointegration and Unit Roots | |
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An Illustrative Example | |
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Estimating the Cointegrating Vector | |
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Intercepts and More Lags | |
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PROC VARMAX | |
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Interpreting the Estimates | |
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Diagnostics and Forecasts | |
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State Space Modeling | |
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Introduction | |
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Some Simple Univariate Examples | |
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A Simple Multivariate Example | |
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Equivalence of State Space and Vector ARMA Models | |
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More Examples | |
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Some Univariate Examples | |
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ARMA(1,1) of Dimension 2 | |
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PROC STATESPACE | |
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State Vectors Determined from Covariances | |
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Canonical Correlations | |
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Simulated Example | |
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Spectral Analysis | |
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Periodic Data: Introduction | |
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Example: Plant Enzyme Activity | |
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PROC SPECTRA Introduced | |
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Testing for White Noise | |
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Harmonic Frequencies | |
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Extremely Fast Fluctuations and Aliasing | |
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The Spectral Density | |
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Some Mathematical Detail (Optional Reading) | |
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Estimating the Spectrum: The Smoothed Periodogram | |
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Cross-Spectral Analysis | |
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Interpreting Cross-Spectral Quantities | |
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Interpreting Cross-Amplitude and Phase Spectra | |
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PROC SPECTRA Statements | |
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Cross-Spectral Analysis of the Neuse River Data | |
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Details on Gain, Phase, and Pure Delay | |
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Data Mining and Forecasting | |
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Introduction | |
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Forecasting Data Model | |
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The Time Series Forecasting System | |
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HPF Procedure | |
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Scorecard Development | |
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Business Goal Performance Metrics | |
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Graphical Displays | |
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Goal-Seeking Model Development | |
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Summary | |
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References | |
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Index | |