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?

Example

Let's take an example to better understand how it works: Imagine that you want to forecast your future sales by country and by product.

To build a predictive model with distinct forecast per entities taking your planning model as data source, Smart Predict needs to match the data contained in your planning model (actuals are used as historical data ) with the variable roles that are mandatory to generate the predictive forecasts:

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).