Restrictions

The following restrictions currently apply to Smart Predict.

General Restrictions While Using Smart Predict

Restriction on	Information on Restrictions
Availability of Smart Predict	Smart Predict is available in all the 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: 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 Integration Suite 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. 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. Note 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 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 select in your time series predictive scenarios with a dataset as your data source, every date format should 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. 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 forecast periods (independent of the number of entities): 500 maximum Number of entities: 1000 maximum If your predictive model is configured for a number of forecast periods 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 forecast periods that can be set is restricted to 500.
Time Series Forecasts	Smart Predict time series forecasts don't persist 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>`. Note 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	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 SECONDDATE TIMESTAMP For more information, see Datetime Data Type.