Článek
10/01/2010

Lesson 1: Creating a Time Series Mining Model and Mining Structure

In this lesson, you will create a mining model that allows you to predict values over time, based on historical data. When you create the model, the underlying structure will be generated automatically and can be used as the basis for additional mining models.

This lesson assumes that you are familiar with forecasting models and with the requirements of the Microsoft Time Series algorithm. For more information, see Microsoft Time Series Algorithm.

CREATE MINING MODEL Statement

In order to create a mining model directly and automatically generate the underlying mining structure, you use the CREATE MINING MODEL (DMX) statement. The code in the statement can be broken into the following parts:

Naming the model
Defining the time stamp
Defining the optional series key column
Defining the predictable attribute or attributes

The following is a generic example of the CREATE MINING MODEL statement:

CREATE MINING MODEL [<Mining Structure Name>]
(
   <key columns>,
   <predictable attribute columns>
)
USING <algorithm name>([parameter list])
WITH DRILLTHROUGH

The first line of the code defines the name of the mining model:

CREATE MINING MODEL [Mining Model Name]

Analysis Services automatically generates a name for the underlying structure, by appending "_structure" to the model name, which ensures that the structure name is unique from the model name. For information about naming an object in DMX, see Identifiers (DMX).

The next line of the code defines the key column for the mining model, which in the case of a time series model uniquely identifies a time step in the source data. The time step I identified with the KEY TIME keywords after the column name and data types. If the time series model has a separate series key, it is identified by using the KEY keyword.

<key columns>

The next line of the code is used to define the columns in the model that will be predicted. You can have multiple predictable attributes in a single mining model. When there are multiple predictable attributes, the Microsoft Time Series algorithm generates a separate analysis for each series:

<predictable attribute columns>

Lesson Tasks

You will perform the following tasks in this lesson:

Create a new blank query
Alter the query to create the mining model
Execute the query

Creating the Query

The first step is to connect to an instance of Analysis Services and create a new DMX query in SQL Server Management Studio.

To create a new DMX query in SQL Server Management Studio

Open SQL Server Management Studio.
In the Connect to Server dialog box, for Server type, select Analysis Services. In Server name, type LocalHost, or the name of the instance of Analysis Services that you want to connect to for this lesson. Click Connect.
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.

Altering the Query

The next step is to modify the CREATE MINING MODEL statement to create the mining model used for forecasting, together with its underlying mining structure.

To customize the CREATE MINING MODEL statement

In Query Editor, copy the generic example of the CREATE MINING MODEL statement into the blank query.

Replace the following:

[mining model name]

with:

[Forecasting_MIXED]

Replace the following:
```
<key columns>
```
with:
```
[Reporting Date] DATE KEY TIME,
[Model Region] TEXT KEY
```
The TIME KEY keyword indicates that the ReportingDate column contains the time step values used to order the values. Time steps can be dates and times, integers, or any ordered data type, so long as the values are unique and the data is sorted.

The TEXT and KEY keywords indicate that the ModelRegion column contains an additional series key. You can have only one series key, and the values in the column must be distinct.

Replace the following:

< predictable attribute columns> )

with:

    [Quantity] LONG CONTINUOUS PREDICT,
    [Amount] DOUBLE CONTINUOUS PREDICT
    )

Replace the following:
```
USING <algorithm name>([parameter list])
WITH DRILLTHROUGH
```
with:
```
USING Microsoft_Time_Series(AUTO_DETECT_PERIODICITY = 0.8, FORECAST_METHOD = 'MIXED')
WITH DRILLTHROUGH
```
The algorithm parameter, AUTO_DETECT_PERIODICITY = 0.8, indicates that you want the algorithm to detect cycles in the data. Setting this value closer to 1 favors the discovery of many patterns but can slow processing.

The algorithm parameter, FORECAST_METHOD, indicates whether you want the data to be analyzed using ARTXP, ARIMA, or a mixture of both.

The keyword, WITH DRILLTHROUGH, specify that you want to be able to view detailed statistics in the source data after the model is complete. You must add this clause if you want to browse the model by using the Microsoft Time Series Viewer. It is not required for prediction.

The complete statement should now be as follows:
```
CREATE MINING MODEL [Forecasting_MIXED]
     (
    [Reporting Date] DATE KEY TIME,
    [Model Region] TEXT KEY,
    [Quantity] LONG CONTINUOUS PREDICT,
    [Amount] DOUBLE CONTINUOUS PREDICT
    )
USING Microsoft_Time_Series (AUTO_DETECT_PERIODICITY = 0.8, FORECAST_METHOD = 'MIXED')
WITH DRILLTHROUGH
```
On the File menu, click Save DMXQuery1.dmx As.
In the Save As dialog box, browse to the appropriate folder, and name the file Forecasting_MIXED.dmx.

Executing the Query

The final step is to execute the query. After a query is created and saved, it needs to be executed to create the mining model and its mining structure on the server. For more information about executing queries in Query Editor, see SQL Server Management Studio Database Engine Query Editor Window.

To execute the query

In Query Editor, on the toolbar, click Execute.

The status of the query is displayed in the Messages tab at the bottom of Query Editor after the statement finishes executing. Messages should display:
```
Executing the query 
Execution complete
```
A new structure named Forecasting_MIXED_Structure now exists on the server, together with the related mining model Forecasting_MIXED.

In the next lesson, you will add a mining model to the Forecasting_MIXED mining structure that you just created.

Next Lesson

Lesson 2: Adding Mining Models to the Time Series Mining Structure

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