This topic describes the anomaly types that are available in the results of time series forecasting.

The results of time series forecasting are all stored in a Logstore named internal-ml-log. You can use the __tag__:__data_type__ field in the results to analyze anomaly types. For more information, see Result fields.
Judgment conditionAnomaly type
  • The value of the __tag__:__data_type__ field is job_error_message.
  • The result.entity and result.metric fields are not empty.
An exception occurs during the forecasting of a time series.

You can use the result.error_type and result.error_msg fields to view details about the exception.

  • The value of the __tag__:__data_type__ field is job_error_message.
  • The result.entity and result.metric fields are empty.

An exception occurs in the forecasting operation indicated by an forecasting ID.

You can use the result.error_type and result.error_msg fields to view details about the exception.