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 poor model performance.
  
  Solution:
  
  Data Validation: Check the input data for completeness and accuracy. Ensure that there are no missing values or outliers that could skew the results.
  Model Review: Review the configuration of the predictive model. Ensure that all parameters are set correctly and that the model is appropriate for the data being used.
  Increase Data Volume: If possible, increase the amount of historical data available for the model. More data can help improve the stability and accuracy of the forecast.
  Algorithm Adjustment: Consider using a different forecasting algorithm that may be better suited to the data characteristics.
  Consult Documentation: Refer to SAP documentation or support resources for specific guidance on the error and recommended practices for resolving it.
  
  Related Information:
  
  SAP Notes and Knowledge Base Articles: Check SAP's support portal for any notes or articles related to UPF1011 for additional troubleshooting steps or patches.
  Community Forums: Engage with the SAP community forums where other users may have encountered similar issues and can provide insights or solutions.
  Training and Best Practices: Consider training on SAP IBP or SAC to better understand the forecasting models and how to configure them effectively.
  
  If the issue persists after trying the above solutions, it may be beneficial to reach out to SAP support for further assistance.