Type of predictive models |
For
Smart Predict - Predictive Planning, i.e. the
integration of SAP Analytics Cloud Smart Predict with SAP
Analytics Cloud planning models, only time series forecasting is
supported. |
Input planning models |
- Type:
Smart Predict - Predictive Planning
only supports standalone planning models (both new model
types and classic account models). All data sources that
can be leveraged by SAP Analytics Cloud planning models
are automatically supported, with the exception of SAP
BPC. Neither live nor acquired SAP Business Planning and
Consolidation (SAP BPC) planning models are
supported.
- Version: You can use public or
private versions. The input version must be a public
version, not in edit mode, or a private version. You
have at least a read access to it.
Note
For
classic account models, there
are specificities while using public or private
versions when currency conversion is enabled. For
more information, see Use Currencies with Predictive Planning.
- Prompt: Planning models with
prompts are supported. If prompts are present, default
prompt values (set by the user or SAP Analytics
Cloud defaults) are used to query the data.
There is no way to set or change values.
|
Entities (crossing of multiple dimensions) For more
information on entities, see also Get Distinct Predictive Forecasts per Entities For your Planning Model
|
|
Entity Filters |
-
(Null) and (No
Value) values are not supported for
attributes and hence not listed for member selection
in Available Members.
-
When defining a filter, a maximum of 1000 values are
displayed under the list of Available
Members for selection.
If the selection dimension has more members, the user
has to make a search limited to the list of
Available Members and the
search displays a max of 1000 results.
|
Predictive Goal |
- These are Smart Predict - Predictive
Planning settings available:
- Target: a valid target is a
numeric
value that is data entry
enabled. A
numeric
value which involves formulas,
or with aggregation type LABEL or NONE is not a
valid target.
Supported
numeric values are leaf members
in the account dimension hierarchy, with no
formula, or a parent numeric value with
aggregation type SUM, or no aggregation defined
(defaults to SUM) provided that none of its
descendants is a member which involves a formula,
or with aggregation type LABEL or
NONE.
Smart
Predict doesn't support calculated measures when
using a planning model, even if an inverse formula
is provided. For more information on inverse
formulas, you can refer to the chapter Inverse Formulas.
- Time granularity: indicates
the time granularity inherited from the planning
model data source. If the time granularity defined
in the planning model date dimension is different
from the time granularity of the data, the time
granularity of the data is used when creating
predictive forecasts. For more detail you can
refer to the section below called Time
Aggregation.
- Date: the date dimension
used to create the time series predictive
model.
-
Note
Only the Date dimension
is used as as influencer. All other dimensions,
attributes or measures are ignored when you select
Train &
Forecast.
- Dimensions or Attributes:
you can combine multiple dimensions and attributes
to use as source for the entities to get distinct
predictive forecasts per entities.
- Number of Forecasts periods: number
of forecast values you want to get. The number of
historical data points in the planning model conditions
the number of confident predictive forecasts you can
get. The current ratio is 5 to 1. For example, if you
want one confident forecast, you need at least five
historical data points in your planning model. For more
information, see How Many Forecasts can be Requested?.
|
Outputs |
- Output versions:
- You can write-back the predictions only on
private versions of planning models. The private
versions have to be created beforehand in stories.
For more information, see About Version Management.
- You cannot write-back the predictive forecasts
in a dataset.
|
Time aggregation |
Time granularity: The time series
predictive model is trained and applied based on the level of
time granularity available in the planning model data source.
When
creating a planning model, the time granularity of the date
dimension can be either Year,
Quarter, Month
or Day.
So, as a simple
example, if the planning model's lowest level of time
granularity is monthly, then Smart Predict
creates monthly predictive forecasts. Example
-
The granularity of the date dimension of your
planning model is defined as monthly.
-
You have data for the months from January 2016
to December 2020 - 60 months.
-
You ask Smart Predict for 12
forecasts.
In this case, Smart Predict
generates forecasts from January 2021 to December
2021.
In a more complex scenario,
the time granularity defined in the planning model date
dimension can be different from the time granularity of the
data. A planning model could have a daily time granularity
in the date dimension, but the data could be stored every
month, or every week. In this case, Smart
Predict - Predictive Planning gives priority to
the time granularity of the data. Example
-
You have a planning model with daily
granularity in the date dimension – from January
1st 2016 to December 31st 2021.
-
The data is stored at a monthly level, so you
have one row of data for January 2016, one row for
February 2016 etc.
-
If you ask Smart Predict for 12
forecasts, they are generated for January 2021 to
December 2021, not for January 1st 2021 to January
12th 2021.
|
Publishing to PAi |
It's not currently possible to publish a predictive model
created from a planning model data source to a PAi application.
|
Spreading |
The spreading policy is the default policy available in
the planning model data source. It depends on how the
dimension is used in the model:
- Date Dimension: no spreading occurs on date
dimension as predictive forecasts are generated
using data at the lowest level of granularity for
the date dimension.
- Dimension used as part of the entity definition: no
spreading occurs.
- Dimension attribute used as part of the entity
definition: spreading occurs no matter if this
attribute is used as a level-based hierarchy or
not.
- Dimension not used as part of the entity definition:
spreading occurs. If the target cell (in the
horizon) is unbooked, the value is allocated to the
Unassigned member of this dimension. Whereas if the
target cell is booked, the value will be split
according to existing weights.
For example, if your planning model data source has defined
a spreading and if you have run the predictive forecasts on a
parent node (for example, <All Regions>),
results are automatically spread across all levels below (for
example, <North America>,
<EMEA>, and then <all
countries> below, then <all
cities>, etc..). For more information, you can
refer to Spreading a Value, Entering Values in a Table, and
Disaggregation of Values during Data
Entry. |