Contents
Part 1: Time Series as a Subject for Analysis
1.2 Types of Time Series: Theoretical Considerations
1.3 Types of Time Series: Practical Considerations
1.4 Time Series Procedures in SAS
1.5 References for Data Used in this Book
Chapter 2 Datetime Variables in SAS
2.3 Importing Datetime Variables
2.4 Handling Datetime Variables
Chapter 3 Aggregation Using PROC TIMESERIES
Chapter 4 Interpolation Using PROC EXPAND
4.1 Interpolation of Time Series
Chapter 5 Exponential Smoothing of Nonseasonal Series
5.1 Simple Exponential Smoothing
5.2 Double Exponential Smoothing
5.3 Forecasting Danish Fertility by Exponential Smoothing
5.5 Forecast Errors for the Prediction of Danish Fertility
5.6 Moving Average Representations
5.7 Calculating Confidence Limits for Forecasts
5.8 Applying Confidence Limits for Forecasts
5.9 Confidence Limits for Forecasts of Danish Fertility
5.10 Determining the Smoothing Constant
5.11 Estimating the Smoothing Parameter in PROC ESM
5.12 Holt Exponential Smoothing and the Damped-Trend Method
5.13 Forecasting Fertility by the Damped-Trend Method in PROC ESM
5.14 Concluding Remarks about Exponential Smoothing for Forecasting
Chapter 6 Forecasting by Exponential Smoothing of Seasonal Series
6.1 Seasonal Exponential Smoothing
6.2 Using the Winters Method for Seasonal Forecasting
6.3 Forecasting the Number of Overnight Stays by US Citizens at Danish Hotels
6.4 Forecasting Using Additive Seasonal Exponential Smoothing with PROC ESM
6.5 Forecasting US Retail E-Commerce Using the Winters Method
6.6 Forecasting the Relative Importance of E-Commerce by PROC ESM
6.7 Forecasting the Relative Importance of E-Commerce Using a Transformation in PROC ESM
Chapter 7 Exponential Smoothing versus Parameterized Models
7.1 Exponential Smoothing Expressed as Autoregressive Models
7.3 Fitting Autoregressive Models
7.6 Estimating Box-Jenkins ARIMA Models in SAS
7.7 Forecasting Fertility Using Fitted ARMA Models in PROC VARMAX
7.8 Forecasting the Swiss Business Indicator with PROC ESM
7.9 Fitting Models for the Swiss Business Indicator Using PROC VARMAX
Chapter 8 Basic Adjustments Using the Census X11 Method
8.2 Seasonal Adjustment Using Census X11
8.3 Seasonal Adjustment of US E-Commerce
8.4 Seasonal Adjustment of UK Unemployment
Chapter 9 Additional Facilities in PROC X12
9.1 Model Fitting and Forecasting Using PROC X12
9.2 Seasonal Adjustment of US E-Commerce Data Using the Additional Features in PROC X12
9.3 Seasonal Adjustment of the Number of Overnight Stays
Part 5: Unobserved Components Models
Chapter 10 Models with Unobserved Components
10.1 Formulation of the Basic Model
10.4 Estimation of Unobserved Components Models
10.5 State Space Models in SAS
Chapter 11 Analysis of Danish Fertility Using PROC UCM
Chapter 12 Analysis of US E-Commerce Using PROC UCM
12.1 Estimation of the Components
Chapter 13 An Analysis of the Arctic Ice Coverage Series Using Unobserved Components
13.2 Aggregation to Yearly Averages
13.3 Aggregation to Monthly Averages
13.4 Aggregation to Weekly Averages
13.5 Aggregation to a Series Observed Every Second Day