Outline of the Forecasting Process (2024)

Getting Started with Time Series Forecasting

The examples shown in the following sections illustratethe basic process you will use with the Forecasting System.

Specify the Input Data Set

Suppose you have a number of time series, variables recorded over time,for which you want to forecast future values.The past values of these time series are stored as variablesin a SAS data set or data view.The observations of this data set correspond to regular time periods,such as days, weeks, or months.The first step in the forecasting process is to tell the system to usethis data set by setting the Data Set field.

If your time series are not in a SAS data set,you must provide a way for the SAS System to access the data.You can use SAS features to read your data into a SAS data set;refer to SAS Language Reference.You can use a SAS/ACCESS product to establish a view of data in adatabase management system; refer to SAS/ACCESS documentation.You can use PROC SQL to create a SAS data view.You can use PROC DATASOURCE to read data from files supplied by supporteddata vendors; refer to Chapter 10, "The DATASOURCE Procedure," for more details.

Provide a Valid Time ID Variable

To use the Forecasting System, your data set must be dated:the data set must contain a time ID variable that gives thedate of each observation.The time ID variable must represent the observation dates withSAS date valuesor with SAS datetime values(for hourly data or other frequencies less than a day),or you can use a simple time index.

When SAS date values are used,the ID variable contains dates withinthe time periods corresponding to the observations.For example, for monthly data, the values for the time ID variable may bethe date of the first day of the month corresponding to each observation,or the time ID variable may contain the date of the last day in the month.(Any date within the period will serve as the time ID for the observation.)

If your data set already contains a valid time ID variablewith SAS date or datetime values,the next step is to specify this time ID variable in the Time ID field.If the time ID variable is named DATE,the system fills in the Time ID field automatically.

If your data set does not contain a time ID, you mustadd a valid time ID variable before beginning the forecasting process.The Forecasting System provides features that make this easy to do.See Chapter 24, "Creating Time ID Variables," for details.

Select and Fit a Forecasting Model for each Series

If you are using the automated model selection feature,the system performs this step for you and chooses a forecastingmodel for each series automatically.All you need to do is select the Fit Models Automatically buttonand then select the variables to fit models for.

If you want more control over forecasting model selection,you can select the Develop Models button,select the series you want to forecast,and use the Develop Models window to specify a forecasting model.As part of this process, you may use the Time Series Viewerand Model Viewer graphical tools.Once you have selected a model for the first series,you can select a different series to work withand repeat the model development process until you havecreated forecasting models for all the series you want to forecast.

The system provides many features to help you choose the bestforecasting model for each series. The features of the Develop Modelswindow and graphical viewer tools are introduced in later sections.

Produce the Forecasts

Once a forecasting model has been fit for each series,select the Produce Forecasts buttonand use the Produce Forecasts window to compute forecastvalues and store them in a SAS data set.

Save Your Work

If you want only a single forecast, your task is now complete.But you may want to produce updated forecasts later,as more data becomes available.In this case, you want to save the forecasting models youhave created, so that you will not need to repeat the modelselection and fitting process.

To save your work, fill in the Project fieldwith the name of a SAS catalog member in which the systemwill store the model information when you exit the system.Later, you will select the same catalog member name whenyou first enter the Forecasting System, and the model informationwill be reloaded.

Note that any number of people can work with the same project file.If you are working on a forecasting project as part of a team,you should take care to avoid conflicting updates to the project fileby different team members.

Summary

This is the basic outline of how the Forecasting System works.The system offers many other features and options that you may need to use(for example, the time range of the data used to fit models and how far intothe future to forecast).These options will become apparent as you work with the Forecasting System.

As an introductory example,the following sections use theAutomatic Model Fitting and Produce Forecasts windowsto perform automated forecasting of theseries in an example data set.

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