Name

adams.flow.transformer.TimeseriesFeatureGenerator

Synopsis

Applies a timeseries feature generator to the incoming timeseries and outputs the generated features.

Additional information

Flow input/output:
- input: adams.data.timeseries.Timeseries
- output: adams.data.spreadsheet.Row

Options

• loggingLevel
  

  The logging level for outputting errors and debugging output.

  command-line	-logging-level <OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST>
  default	WARNING
  min-user-mode	Expert

  
  
• name
  

  The name of the actor.

  command-line	-name <java.lang.String>
  default	TimeseriesFeatureGenerator

  
  
• annotations
  

  The annotations to attach to this actor.

  command-line	-annotation <adams.core.base.BaseAnnotation>
  default

  
  
• skip
  

  If set to true, transformation is skipped and the input token is just forwarded as it is.

  command-line	-skip <boolean>
  default	false

  
  
• stopFlowOnError
  

  If set to true, the flow execution at this level gets stopped in case this actor encounters an error; the error gets propagated; useful for critical actors.

  command-line	-stop-flow-on-error <boolean>
  default	false
  min-user-mode	Expert

  
  
• silent
  

  If enabled, then no errors are output in the console; Note: the enclosing actor handler must have this enabled as well.

  command-line	-silent <boolean>
  default	false
  min-user-mode	Expert

  
  
• algorithm
  

  The feature generation algorithm to use.

  command-line	-algorithm <adams.data.timeseries.AbstractTimeseriesFeatureGenerator>
  default	adams.data.timeseries.Values -converter \"adams.data.featureconverter.SpreadSheet -data-row-type adams.data.spreadsheet.DenseDataRow -spreadsheet-type adams.data.spreadsheet.DefaultSpreadSheet\"

  
  
• variableName
  

  The variable to monitor for resetting trainable batch filters.

  command-line	-var-name <adams.core.VariableName>
  default	variable