How To Fix /SAPAPO/PR414 - Historical time series shows irregular pattern

/SAPAPO/PR414 - Overview

• Message type: E = Error

• Message class: /SAPAPO/PR -

• Message number: 414

• Message text: Historical time series shows irregular pattern

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 /SAPAPO/PR414 - Historical time series shows irregular pattern ?

  The SAP error message /SAPAPO/PR414 Historical time series shows irregular pattern typically occurs in the context of SAP Advanced Planning and Optimization (APO) when the system detects that the historical data for a time series does not follow a regular or expected pattern. This can hinder forecasting and planning processes.
  Causes:
  
  Irregular Data Points: The historical data may have missing values, outliers, or irregular intervals that disrupt the expected pattern.
  Seasonality Issues: If the data is expected to have a seasonal pattern but does not, it can trigger this error.
  Data Entry Errors: Incorrect or inconsistent data entry can lead to irregularities in the time series.
  Insufficient Historical Data: A lack of sufficient historical data points can make it difficult for the system to identify a pattern.
  Changes in Business Processes: Changes in production, sales, or other business processes can lead to irregularities in historical data.
  
  Solutions:
  
  Data Cleansing: Review and clean the historical data to remove outliers