Adding a Data Source View for Forecasting (Intermediate Data Mining Tutorial)

In this task, you add a new data source view that will be used for the forecasting scenario. A forecasting model requires that the data contains a column that can be used to identify steps in a time series. If you plan to analyze multiple series of data, all series must end on the same date or time step.

To add a data source view

  1. In Solution Explorer, right-click Data Source Views, and select New Data Source View.

  2. On the Welcome to the Data Source View Wizard page, click Next.

  3. On the Select a Data Source page, under Relational data sources, select the Adventure Works DW2008 data source. Click Next.


    If you do not have this data source, the steps to create the data source are described in the Basic Data Mining Tutorial.

  4. On the Select Tables and Views page, select the table, vTimeSeries (dbo), and then click the right arrow to add it to the data source view.

  5. Click Next.

  6. On the Completing the Wizard page, by default the data source view is named Adventure Works DW2008. Change this to SalesByRegion, and then click Finish.

    Data Source View Designer opens to display the SalesByRegion data source view.

Working with the Data Source View

After you have created the data source view, you can explore the data in the following ways:

  • Sampling data randomly, or taking the top n rows.

  • Assigning friendly names to the tables or columns in the data source view.

  • Viewing charts that show the distribution of the data.

  • Creating aggregations on the fly with pivot charts and pivot tables.

In the next task, you will explore the time series data by creating a simple pivot tables in Business Intelligence Development Studio. You will also set an optional property that handles gaps in data. 

If you are already familiar with the requirements for time series models and know how to use Business Intelligence Development Studio to build rich data tables, you can skip to the task, Creating a Forecasting Structure and Model (Intermediate Data Mining Tutorial).