The following restrictions currently apply to Smart Predict.
|Restriction on||Information on Restrictions|
|Predictive scenario migration - creation dates not kept||When predictive scenarios are moved from the Browse Predictive Scenario page to the Files area, the individual creation dates are not kept. The predictive scenario creation date that is displayed after the move to Files is the timestamp for the move operation.|
|Availability of Smart Predict||
Smart Predict is available in most regions and for most tenant types.
For more details on exceptions and general availability, refer to the SAP Note 2661746 .
|Data Sources (acquired datasets and planning models)||
You can create predictive scenarios on datasets that use the following data sources:
|Dataset - Storage formats||
Time variables are currently not supported by Smart Predict. If your dataset (acquired or live dataset) contains a column that contains only time variables, this column won't be included in the training process.
|Dataset Maximum Sizes and Limits||See System Sizing, Tuning, and Limits|
|Time Series Predictive Scenario||
|Time Series Forecasts||Smart Predict time series forecasts don't respect the settings for Number Formatting selected by the user in the User Preferences section of SAP Analytics Cloud Profile Settings.|
|Classification Predictive Scenario||In a classification predictive scenario, the target can only be a binary column that only takes two values, for example, true or false, yes or no, male or female, 0 or 1. For this type of scenario, Smart Predict considers that the positive target value, or positive target category of this column, is the least frequently occurring value in the training dataset. However, to make sure your trained predictive model is reliable, you need to make sure that you have a minimum representation in your training dataset. For example, if your dataset contains very few failures, your predictive model won’t be able to predict the under-represented category failures.|
|Training a Predictive Model||Smart Predict currently excludes the following
columns when training your predictive model:
For more information, see Variables in Smart Predict
|Restrictions on||Information on Restrictions|
|SAP HANA SQL Views using row-level security||You should not allow the creation of live datasets on top of SAP HANA SQL Views using row-level security (see Structure of SQL-Based Analytic Privileges). In Smart Predict you access the dataset using the SAP HANA technical user configured at the data repository level, and not using the SAP Analytics Cloud user profile. This could result in a security issue as all SAP Analytics Cloud users would get access to the data accessible by the SAP HANA technical user. For more information, see Configuring a SAP HANA technical User in the On-Premise SAP HANA System.|
|Number of columns for live datasets||There is a limit of 1000 columns when using live datasets with predictive models.|
|Live Data Sources||
You can create predictive scenarios on live datasets in the following on-premise SAP HANA systems:
Note: Cloud deployments of SAP HANA systems are currently not supported.
|Privileges for a SAP HANA technical user||A maximum of 4000 tables/SQL views are displayed for creating a live dataset through browsing. It is recommended that the SELECT privileges for a SAP HANA technical user are limited to only tables/SQL views required for the predictive models. For more information, see Configuring a SAP HANA technical User in the On-Premise SAP HANA System|
|BI story||You can't directly create a BI Story on top of a live dataset whether or not this live dataset was created with Smart Predict. For more information, refer to Creating Calculation Views to Consume Live Output Datasets|
|Train and Apply steps with live datasets||Train or Apply operations using live datasets that last longer than 8 hours, don't complete.|
For live datasets, the following default SAP HANA date formats are supported:
|Restrictions on||Information on Restrictions|
|Type of predictive models||Planning model data sources can only be used for time series predictive models.|
|Input planning models||
|Entities (crossing of multiple dimensions)
For more information on entities, see also How Can You Get Distinct Predictive Forecasts per Entities For Your Planning Model?
|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. If the planning model lowest level is daily, then Smart Predict will create daily predictive forecasts.|
|Publishing to PAi||It's not currently possible to publish a predictive model created from a planning model data source to a PAi application.|
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: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.