The following restrictions currently apply to Smart Predict.

General Restrictions While Using 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 Information published on SAP site.

Data Sources (acquired datasets and planning models)

You can create predictive scenarios on datasets that use the following data sources:

  • local file (.txt, .csv, .xlsx)
    Files with extension .xls are not supported.
  • SAP Business ByDesign Analytics
  • SAP Cloud for Customer
  • SAP Cloud for Customer Analytics
  • SAP Integrated Business Planning
  • SAP SuccessFactors
  • SAP Qualtrics
  • OData Services
  • SQL Databases
  • SAP BW
    We recommend you to upgrade your SAP Analytics Cloud version to 1.0.43 to have drop parent hierarchy nodes functionality. Although you can import dataset with a lower C4AAgent version, hierarchy selection will be disabled and a corresponding message will be shown in query builder.
  • Google Drive
  • SAP Cloud Platform Open Connectors
Dataset - Storage formats
  • The following data types are currently supported:
    • Date and Date & Time, in the following formats:
      • YYYY-MM-DD
      • YYYY/MM/DD
      • YYYY/MM-DD
      • YYYY-MM/DD
      • YYYYMMDD
      • YYYY-MM-DD hh:mm:ss
      Where YYYY stands for the year, MM for the month, and DD for the day of the month, hh stands for hours from 0 to 24, mm stands for minutes from 0 to 59, and ss stands for seconds from 0 to 59.
      January 25, 2018 will take one of the following supported formats:
      • 2018-01-25
      • 2018/01/25
      • 2018/01-25
      • 2018-01/25
      • 20180125
    • Numbers (any number with decimal point)
    • Integers (any number without decimal point)
  • The column name restrictions are the same as the SAP HANA ones. If some characters are not supported, the column name is automatically converted to a supported name. The original name is kept as a column description in the metadata.
  • UTF-8 encoding is supported.

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
  • The Date and Date & Time formats that should be used in your dataset are the following:
    • YYYY-MM-DD
    • YYYY/MM/DD
    • YYYY/MM-DD
    • YYYY-MM/DD
    • YYYY-MM-DD hh:mm:ss
      While you can use this format in both live and acquired datasets, the seconds (ss) won't be taken into account during the training of your predictive models.
    Where YYYY stands for years, MM stands for months, DD stands for day of the month, hh stands for hours from 0 to 24, mm stands for minutes from 1 to 59, and ss stands for seconds from 0 to 59.
    Regardless of the date granularity you choose in your time series predictive scenarios with a dataset as your data source, every date format has to include years, months and days. This means that even if you just want a quarterly or monthly forecast, the date format in your dataset still needs to include days.

    If your data source is a planning model, you can use the YYYY-MM date format.

    Let's say you use the YYYY-MM-DD date format, you can create time series predictive scenarios where the date granularity can be:
    • Year expressed as YYYY-01-01 where YYYY is variable (moving year).
    • Quarter or Month expressed as YYYY-MM-01 where YYYY-MM is variable (moving month).
    • Weekly data in the date format YYYY-MM-DD taking for instance the 1st day of the week as the characters DD (moving week).
    • Day (calendar dates) expressed as YYYY-MM-DD where YYYY-MM-DD is variable (moving day).
  • Smart Predict expects a date per each period to learn on: if you want to forecast your monthly sales, you provide a date per month representing the value of the corresponding month.
  • In a time series predictive scenario, you can define entities, each generating its specific predictive model simultaneously.

    For example, if you define a column with countries as an entity, Smart Predict will generate as many predictive models as there are countries in your data source.

  • The following limits are recommended when using a time series forecast model:

    • Number of forecasts (independent of the number of entities): 400 maximum
    • Number of entities: 1000 maximum
    If your predictive model is configured for a number of forecasts and/or entities beyond the recommended maximum limits, it is likely to create performance issues that can impact other users on the same SAP Analytics Cloud tenant. In the user interface, the maximum number of forecasts that can be set is restricted to 400.
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:
  • Columns identified as having the data type <Time>.

    Date & Time is supported by Smart Predict.

  • Columns with the data type <Textual>.

For more information, see Variables in Smart Predict

Restrictions Using Live Dataset With Smart Predict
Please note the following restrictions when using live datasets with your predictive models:
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:

  • SAP HANA 1.0 SPSP12 rev 122.04 and upwards
  • SAP HANA 2.0 SPS00, SPS01, and SPS02
  • SAP HANA 2.0 SPS03 and upwards

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.
Date Format
For live datasets, the following default SAP HANA date formats are supported:
  • DATE
For more information, see Datetime Data Type.
Restrictions Using Planning Model as Data Source for Smart Predict
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
  • Type: Smart Predict supports only standalone planning models. BPC planning models are not supported whether these are live or acquired.
  • 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.
    There are specificities while using public or private versions when currency conversion is enabled. For more information, see How does Smart Predict Support Currencies Defined in Planning Model?.
  • 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 How Can You Get Distinct Predictive Forecasts per Entities For Your Planning Model?

  • You cannot select more than 5 dimensions or attributes to create a time series predictive model generating predictive forecasts per entities.
  • The maximum number of entities supported is 1000.

  • Attributes and Hierarchies:
    • You can select the attributes (custom properties) that form part of level-based hierarchies or that can be freely defined.
    • You can select system properties like the currency for instance.
    • The levels can be selected indirectly by selecting the custom properties that form part of the level-based hierarchy, whereas for parent child hierarchies, you need to create them as Smart Predict can generate one segment for each leaf only.
      It's possible to add custom properties to group members in custom ways: you can use this mechanism to keep the number of entities under 1000 and perform an intermediate forecasting approach where predictive forecast is run on intermediate nodes: For nodes above, predictive forecasts will be spread and for nodes below, they will be summed.
  • One entity can combine one or multiple dimension members.

  • Smart Predict supports only the following influencers:
    • Signal: a valid influencer is a measure is a measure that is data entry enabled. A measure which involves formulas, or with aggregation type LABEL or NONE is not valid as signal. Supported measures are base measures (leaf members in the account dimension hierarchy, with no formula) or a parent measure with aggregation type SUM or no aggregation defined (defaults to SUM) provided that none of its descendant is a member which involves a formula, or with aggregation type LABEL or NONE. If you select a non-supported measure, an error with be raised at training time or application time (when writing-back the predictive forecasts).
    • Time granularity: information inherited from the planning model data source that you cannot change.
    • Date: date dimension used to create the time series predictive model.
    • Dimensions or Attributes: you can combine multiple dimensions and attributes to use as source for the entities to get distinct predictive forecasts per entities.
    Additional influencers are not supported.
  • Number of forecasts: 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?.
  • Predictive forecasts: Only the forecasts corresponding to the predictions are written in the planning model. Forecasts corresponding to the training period are not written in the planning model.
  • 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. 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:
  • 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.