How To Fix UPF1011 - Root from mean square error &1

SAP Error Message - Details

• Message type: E = Error

• Message class: UPF1 - Messages SEM-BPS Forecast

• Message number: 011

• Message text: Root from mean square error &1

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

Smart SAP Assistant

• What is the cause and solution for SAP error message UPF1011 - Root from mean square error &1 ?

  The SAP error message UPF1011, which states "Root from mean square error &1," typically relates to issues in the context of predictive analytics or forecasting within SAP systems, particularly in the SAP Integrated Business Planning (IBP) or SAP Analytics Cloud (SAC) environments. This error indicates that there is a problem with the calculation of the root mean square error (RMSE), which is a common metric used to evaluate the accuracy of a predictive model.
  
  Cause:
  
  Data Quality Issues: The underlying data used for the forecast may contain errors, such as missing values, outliers, or incorrect data types.
  Model Configuration: The predictive model may not be configured correctly, leading to issues in the calculation of RMSE.
  Insufficient Data: There may not be enough historical data to generate a reliable forecast, which can lead to instability in the RMSE calculation.
  Algorithm Limitations: The algorithm used for forecasting may not be suitable for the data characteristics, leading to