How Can You Get Distinct Predictive Forecasts per Entities For Your Planning Model?
Thanks to Smart Predict, you can create distinct predictive forecasts per entities using your planning model as data source, where the granularity of the predictive forecasts is determined by the aggregation level of the combined dimensions. But what does it mean?
Roles in Smart Predict | Correspondence in your planning model | Correspondence with our example |
---|---|---|
Signal | Measure that does not involve calculation | It's the measure you want to forecast: <Sales> |
Date | You can have several dates in a planning model. You select one as main date to consider as the date dimension | <Month> |
Entity | It corresponds to dimensions or attributes for which you want predictive forecasts. | <Country> dimension and <Product> dimension |
Before the training phase, the data can be represented as follows:
Once the training is done and the generated predictive forecasts are available, the data look like:
When application is done, the predictive forecasts are added to your planning model. In our example, it means that the generated forecasts for Sales will be added for June and July.
Reporting in SAP Analytics Cloud at a higher level
Even if the time series predictive model is trained and applied based on a lowest level of date granularity, you can still report data at upper level in a story.
Going back to our example: The times series has been generated on months basis. You can report by aggregating the sales by quarters or years (instead of month).