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Part IX: Business Intelligence
Time Series
The time series algorithm predicts the future values for a series of continuous data points
(for example, web traffi c for the next 6 months given traffi c history). Unlike the algorithms
already presented, prediction does not require new cases on which to base the prediction,
just the number of steps to extend the series into the future. Input data must contain a
time key to provide the algorithm’s time attribute.After the algorithm runs, it generates a decision tree for each series forecast. The decision
tree defi nes one or more regions in the forecast and an equation for each region, which
you can review using the Decision Tree Viewer. The Tree pull-down at the top of the viewer
enables the models for different series to be examined. Each node also displays a diamond
chart whose width denotes the variance of the predicted attribute at that node. In other
words, the narrower the diamond chart, the more accurate the prediction.The second Time Series Viewer, labeled Charts, plots the actual and predicted values of the
selected series over time. Choose the series to be plotted from the drop-down list
in the upper-right corner of the chart. Use the Abs button to toggle between absolute
(series) units and relative (percentage change) values. The Show Deviations check box adds
error bars to display expected variations on the predicted values, and the Prediction Steps
control enables the number of predictions that display. Drag the mouse to highlight the
horizontal portion of interest, and then click within the highlighted area to zoom into that
region. Undo a zoom with the zoom controls on the toolbar.Because prediction is not case-based, the Mining Accuracy Chart does not function for this
algorithm.Cube Integration
Data mining can use Analysis Services cube data as input instead of using a relational table
(see the fi rst page of the Data Mining Wizard section earlier in this chapter); cube data
behaves much the same as relational tables, with some important differences:■ (^) Although a relational table can be included from most any data source, the cube
and the mining structure that references it must be defi ned within the same
project.
■ The case “table” is defi ned by a single dimension and its related measure groups.
When additional data mining attributes are needed, add them via a nested table.
■ Instead of choosing a primary key, choose mining structure keys from dimension
data at the highest (least granular) level possible. For instance, choose the quarter
as the key attribute for quarterly analysis rather than the date key.
■ (^) Data and content type defaults tend to be less reliable for cube data, so review and
adjust type properties as needed.
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