Customizing and Processing the Forecasting Model (Intermediate Data Mining Tutorial)
The Microsoft Time Series algorithm provides parameters that affect how a model is created, and how time data is analyzed. Changing these properties can significantly affect how the mining model makes predictions.
For this task in the tutorial, you will perform the following tasks to modify the model:
You will customize the way your model handles time periods by adding a new value for the PERIODICITY_HINT parameter.
You will learn about two other important parameters for the Microsoft Time Series algorithm: FORECAST_METHOD, which lets you control the method used for forecasting, and PREDICTION_SMOOTHING, which lets you customize the blend of long-term and short-term predictions.
Optionally, you will tell the algorithm how you want missing values to be imputed.
After all the changes have been made, you will deploy and process the model.
Setting Time Series Parameters
Periodicity Hints
The PERIODICITY_HINT parameter provides the algorithm with information about additional time periods that you expect to see in the data. By default, time series models will try to automatically detect a pattern in the data. However, if you already know the expected time cycle, providing a periodicity hint can potentially improve the accuracy of the model. However, if you provide the wrong periodicity hint, it can decrease accuracy; therefore, if you are not sure what value should be used, it is best to use the default.
For example, the view used for this model aggregates sales data from Adventure Works DW Multidimensional 2012 on a monthly basis. Therefore each time slice used by the model represents one month, and all predictions will also be in terms of months. Since there are 12 months in a year and you expect that sales patterns more or less repeat on a yearly basis, you will set the PERIODICITY_HINT parameter to 12, to indicate that 12 time slices (months) constitute one complete sales cycle.
Forecasting Method
The FORECAST_METHOD parameter controls whether the time series algorithm is optimized for short-term or long-term predictions. By default, the FORECAST_METHOD parameter is set to MIXED, which means that two different algorithms are blended and balanced to provide good results for both short-term and long-term prediction.
However, if you know that you want to use a particular algorithm, you can change the value to either ARIMA or ARTXP.
Weighting Long-Term vs. Short-Term Predictions
You can also customize the way that long-term and short-term predictions are combined by using the PREDICTION_SMOOTHING parameter. By default, this parameter is set to 0.5, which generally provides the best balance for overall accuracy.
To change the algorithm parameters
On the Mining Models tab, right-click Forecasting, and select Set Algorithm Parameters.
In the PERIODICITY_HINT row of the Algorithm Parameters dialog box, click the Value column, then type {12}, including the braces.
By default, the algorithm will also add the value {1}.
In the FORECAST_METHOD row, verify that the Value text box is either blank or set to MIXED. If a different value has been entered, type MIXED to change the parameter back to the default value.
In the PREDICTION_SMOOTHING row, verify that the Value text box is either blank or set to 0.5. If a different value has been entered, click Value and type 0.5 to change the parameter back to the default value.
Note
The PREDICTION_SMOOTHING parameter is available only in SQL Server Enterprise. Therefore, you cannot view or change the value of the PREDICTION_SMOOTHING parameter in SQL Server Standard. However, the default behavior is to use both algorithms and weight them equally.
Click OK.
Handling Missing Data (Optional)
In many cases, your sales data might have gaps that are filled with nulls, or a store might have failed to meet the reporting deadline, leaving an empty cell at the end of the series. In such scenarios, Analysis Services raises the following error and will not process the model.
"Error (Data mining): Time stamps not synchronized starting with series <series name>, of the mining model, <model name>. All time series must end at the same time mark and cannot have arbitrarily missing data points. Setting the MISSING_VALUE_SUBSTITUTION parameter to Previous or to a numeric constant will automatically patch missing data points where possible."
To avoid this error, you can specify that Analysis Services automatically provide new values to fill in the gaps by using any one of the following methods:
Using an average value. The mean is calculated by using all valid values in the same data series.
Using the previous value. You can substitute previous values for multiple missing cells, but you cannot fill starting values.
Using a constant value that you supply.
To specify that gaps be filled by averaging values
On the Mining Models tab, right-click the Forecasting column, and select Set Algorithm Parameters.
In the Algorithm Parameters dialog box, in the MISSING_VALUE_SUBSTITUTION row, click the Value column, and type Mean.
Build the Model
To use the model, you must deploy it to a server, and process the model by running the training data through the algorithm.
To process the forecasting model
On the Mining Model menu of SQL Server Data Tools, select Process Mining Structure and All Models.
At the warning asking whether you want to build and deploy the project, click Yes.
In the Process Mining Structure - Forecasting dialog box, click Run.
The Process Progress dialog box opens to display information about model processing. Model processing may take some time.
After processing is complete, click Close to exit the Process Progress dialog box.
Click Close again to exit the Process Mining Structure - Forecasting dialog box.
Next Task in Lesson
Exploring the Forecasting Model (Intermediate Data Mining Tutorial)
See Also
Reference
Microsoft Time Series Algorithm Technical Reference