Comparing Predictions for Forecasting Models (Intermediate Data Mining Tutorial)

You have created the following three models:

• Predictions for each combination of region and model, based only on data for the individual model and region.

• Predictions for all models on a worldwide basis, based on aggregated data.

• Predictions for the M200 model in the North America region, based on the aggregated model.

In this final task, you will contrast the predictions for each model to see how using the generalized model affects the results.

Comparing Prediction Results

Remember that the original mining model showed a large gap between certain regions and model lines. The trend line for the M200 model was particularly high, while the trend lines for the T1000 model were low and relatively flat.

You can create a chart that includes all the predictions by exporting the results and the original data to Microsoft Excel, which provides more sophisticated tools for graphing and managing multiple data series. The following diagram shows the trend lines for just the M200 product models, and compares the predictions from the first mining model against the predictions using the aggregated mining model.

From this chart, you can see that the aggregated mining model smoothens the fluctuations in the individual data series. The following table provides a portion of the data series used to create the chart, to aid in comparison.

Series and Mining Model	7/25/2004	8/25/2004	9/25/2004	10/25/2004	11/25/2004
M200 Europe — aggregated	143	126	115	119	94
M200 Europe—specific	121	142	152	149	154
M200 North America — aggregated	208	150	149	151	172
M200 North America—specific	163	178	156	173	203
M200 Pacific — aggregated	89	80	71	77	57
M200 Pacific—specific	46	44	42	42	38
T1000 Europe — aggregated	65	51	54	53	48
T1000 Europe—specific	42	41	43	42	43
T1000 North America — aggregated	103	84	79	85	68
T1000 North America—specific	82	78	78	83	83
T1000 Pacific — aggregated	68	52	48	56	44
T1000 Pacific—specific	38	39	37	38	36

Conclusion

You have learned how to create a time series model that can be used for prediction, and a generalized model that can be applied to a different data series.

See Also

Concepts

Intermediate Data Mining Tutorials (Analysis Services - Data Mining)