Comparing Predictions for Forecasting Models (Intermediate Data Mining Tutorial)
In the previous steps of this lesson, you 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.
Your analysis of the results of the original mining model revealed 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 the previous chart, you can see that the aggregated mining model preserves the overall trends while minimizing 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
M200 Europe — aggregated
M200 North America — aggregated
M200 North America—specific
M200 Pacific — aggregated
T1000 Europe — aggregated
T1000 North America — aggregated
T1000 North America—specific
T1000 Pacific — aggregated
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.