Lesson 4: Creating Time Series Predictions Using DMX

In this lesson and the following lesson, you will use Data Mining Extensions (DMX) to create different types of predictions based on the time series models that you created in Lesson 1: Creating a Time Series Mining Model and Mining Structure and Lesson 2: Adding Mining Models to the Time Series Mining Structure.

With a time series model, you have many options for making predictions:

  • Use the existing patterns and data in the mining model

  • Use the existing patterns in the mining model but supply new data

  • Add new data to the model or update the model.

The syntax for making these prediction types is summarized below:

Lesson Tasks

You will perform the following tasks in this lesson:

  • Create a query to get the default predictions based on existing data.

In the following lesson you will perform the following related tasks:

  • Create a query to supply new data and get updated predictions.

In addition to creating queries manually by using DMX, you can also create predictions by using the prediction query builder in Business Intelligence Development Studio. For more information, see Using the Prediction Query Builder to Create DMX Prediction Queries or Mining Model Prediction Tab: How-to Topics.

Simple Time Series Prediction Query

The first step is to use the SELECT FROM statement together with the PredictTimeSeries function to create time series predictions. Time series models support a simplified syntax for creating predictions: you do not need to supply any inputs, but only have to specify the number of predictions to create. The following is a generic example of the statement you will use:

SELECT <select list> 
FROM [<mining model name>] 
WHERE [<criteria>]

The select list can contain columns from the model, such as the name of the product line that you are creating the predictions for, or prediction functions, such as Lag (DMX) or PredictTimeSeries (DMX), which are specifically for time series mining models.

To create a simple time series prediction query

  1. In Object Explorer, right-click the instance of Analysis Services, point to New Query, and then click DMX.

    Query Editor opens and contains a new, blank query.

  2. Copy the generic example of the statement into the blank query.

  3. Replace the following:

    <select list> 


    PredictTimeSeries([Forecasting_MIXED].[Quantity],6) AS PredictQty,
    PredictTimeSeries ([Forecasting_MIXED].[Amount],6) AS PredictAmt

    The first line retrieves a value from the mining model that identifies the series.

    The second and third lines use the PredictTimeSeries function. Each line predicts a different attribute, [Quantity] or [Amount]. The numbers after the names of the predictable attributes specify the number of time steps to predict.

    The AS clause is used to provide a name for the column that is returned by each prediction function. If you do not supply an alias, by default both columns are returned with the label, Expression.

  4. Replace the following:

    [<mining model>] 


  5. Replace the following:

    WHERE [criteria>] 


    WHERE [ModelRegion] = 'M200 Europe' OR
    [ModelRegion] = 'M200 Pacific'

    The complete statement should now be as follows:

    PredictTimeSeries([Forecasting_MIXED].[Quantity],6) AS PredictQty,
    PredictTimeSeries ([Forecasting_MIXED].[Amount],6) AS PredictAmt
    WHERE [ModelRegion] = 'M200 Europe' OR
    [ModelRegion] = 'M200 Pacific'
  6. On the File menu, click Save DMXQuery1.dmx As.

  7. In the Save As dialog box, browse to the appropriate folder, and name the file SimpleTimeSeriesPrediction.dmx.

  8. On the toolbar, click the Execute button.

    The query returns 6 predictions for each of the two combinations of product and region that are specified in the WHERE clause.

In the next lesson, you will create a query that supplies new data to the model, and compare the results of that prediction with the one you just created.