Restrictions

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 .

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

• local file (.txt, .csv, .xlsx)
  Note

  Files with extension .xls are not supported.
• SAP Business ByDesign Analytics
• SAP Cloud for Customer
• SAP Cloud for Customer Analytics
• SAP HANA
• SAP Integrated Business Planning
• SAP S/4HANA
• SAP SuccessFactors
• SAP Qualtrics
• OData Services
• SQL Databases
• SAP BW
  Note

  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

• 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.

    Example

    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.

• 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
  • YYYYMMDD
  • YYYY-MM-DD hh:mm:ss
    Note

    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.

  Note

  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.

  Example

  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.

• Columns identified as having the data type <Time>.
  Note

  Date & Time is supported by Smart Predict.

• Columns with the data type <Textual>.

For more information, see Variables in Smart Predict

• 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.
  Restriction

  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.

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.
    Tip

    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.