Outliers in Custom ARIMA Models

The Outliers tab provides the following choices for the handling of outliers 1: detect them automatically, specify particular points as outliers, or do not detect or model them.

Do not detect outliers or model them. By default, outliers are neither detected nor modeled. Select this option to disable any detection or modeling of outliers.

Detect outliers automatically. Select this option to perform automatic detection of outliers, and select one or more of the following outlier types:

  • Additive
  • Level shift
  • Innovational
  • Transient
  • Seasonal additive
  • Local trend
  • Additive patch

Model specific time points as outliers. Select this option to specify particular time points as outliers. Use a separate row of the Outlier Definition grid for each outlier. Enter values for all of the cells in a given row.

  • Type. The outlier type. The supported types are: additive (default), level shift, innovational, transient, seasonal additive, and local trend.

Note 1: If no date specification has been defined for the active dataset, the Outlier Definition grid shows the single column Observation. To specify an outlier, enter the row number (as displayed in the Data Editor) of the relevant case.

Note 2: The Cycle column (if present) in the Outlier Definition grid refers to the value of the CYCLE_ variable in the active dataset.

To Enable Automatic Detection of Outliers or to Specify Explicit Outliers

This feature requires the Forecasting option.

  1. From the menus choose:

    Analyze > Forecasting > Create Models...

  2. On the Variables tab, select ARIMA for Method.
  3. Click Criteria....
  4. Click the Outliers tab.
1 Pena, D., G. C. Tiao, and R. S. Tsay, eds. 2001. A course in time series analysis. New York: John Wiley and Sons.