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