Chapters starting from Chapter 46: Getting Started with Time Series Forecasting, through Chapter 50: Using Predictor Variables, contain a series of example sessions that show the major features of the system. Chapters from Chapter 51: Command Reference, through Chapter 53: Forecasting Process Details, serve as reference and provide more details about how the system operates. The reference chapters contain a complete list of system features.
To get started using the Time Series Forecasting system, it is a good idea to work through a few of the example sessions. Start with Chapter 46: Getting Started with Time Series Forecasting, and use the system to reproduce the steps shown in the examples. Continue with the other chapters when you feel comfortable using the system.
The example sessions make use of time series data sets contained in the SASHELP library: air, citimon, citiqtr, citiyr, citiwk, citiday, gnp, retail, usecon, and workers. You can use these data sets to work through the example sessions or to experiment further with the system.
Once you are familiar with how the system operates, start working with your own data to build your own forecasting models. When you have questions, consult the reference chapters mentioned above for more information about particular features.
The Time Series Forecasting system forecasts time series, that is, variables that consist of ordered observations taken at regular intervals over time. Since the Time Series Forecasting system is a part of the SAS software system, time series values must be stored as variables in a SAS data set or data view, with the observations representing the time periods. The data can also be stored in an external spreadsheet or data base if you license SAS/ACCESS software.
The Time Series Forecasting System chapters refer to series and variables. Since time series are stored as variables in SAS data sets or data views, these terms are used interchangeably. However, the term series is preferred when attention is focused on the sequence of data values, and the term variable is preferred when attention is focused on the data set.