Generate Data (ARIMA) (Time Series)

Synopsis

This operator generates a time series from an ARIMA process.

Description

The process is defined by auto-regressive terms and moving-average terms, which defíne how strongly previous values of the time series influence the next values. The result of the operator is a single attribute that includes the time series.

Differentiation

Generate Data

This operator also generates a new ExampleSet. It offers many different generating functions and can generate ExampleSets with a label attribute.

Output

  • arima (IOObject)

    ExampleSet which has only one attribute that represents the ARIMA time series.

Parameters

  • name_of_new_time_series_attribute

    This parameter sets the name of the new time series attribute which is returned.

    Range:
  • coefficients_of_the_auto-regressive_terms

    This parameter list specifies the coefficients of the auto-regressive terms.

    Range:
  • coefficients_of_the_moving-average_terms

    This parameter list specifies the coefficients of the moving-average terms.

    Range:
  • constant

    This parameters sets a starting point for the ARIMA process.

    Range:
  • standard_deviation_of_the_innovations

    This parameter sets the standard deviation of the innovations. It controls the amount of variation that is added to each new data point.

    Range:
  • length

    This parameter is the final length of the generated time series. It is the number of examples of the new ExampleSet.

    Range:
  • use_local_random_seed

    This parameter indicates if a local random seed should be used. If selected a local seed is used specifically for this operator.

    Range:
  • local_random_seed

    If the use local random seed parameter is checked this parameter determines the local random seed.

    Range:

Tutorial Processes

Generating a sample ARIMA process

Simple process that generates an ARIMA time series.