Determining root causes of outliers

Given a temporal causal model system, it is possible to go beyond outlier detection and determine the series that most likely causes a particular outlier. This process is referred to as outlier root cause analysis and must be requested on a series by series basis. The analysis requires a temporal causal model system and the data that was used to build the system. In this example, the active dataset is the data that was used to build the model system.

To run outlier root cause analysis:

  1. In the TCM dialog, go to the Build Options tab and then click Series to Display in the Select an item list.
    Figure 1. Temporal Causal Model Series to Display
    Temporal Causal Model Series to Display
  2. Move KPI_19 to the Fields to display list.
  3. Click Output options in the Select an item list on the Options tab.
    Figure 2. Temporal Causal Model Output Options
    Temporal Causal Model Output Options
  4. Deselect Overall model system, Same as for targets, R square, and Series transformations.
  5. Select Outlier root cause analysis and keep the existing settings for Output and Causal levels.
  6. Click Run.
  7. Double-click the Outlier Root Cause Analysis chart for KPI_19 in the Viewer to activate it.
    Figure 3. Outlier Root Cause Analysis for KPI_19
    Outlier Root Cause Analysis for KPI_19

The results of the analysis are summarized in the Outliers table. The table shows that root causes were found for the outliers at 2009-04-05 and 2010-09-19, but no root cause was found for the outlier at 2008-10-12. Clicking a row in the Outliers table highlights the path to the root cause series, as shown here for the outlier at 2009-04-05. This action also highlights the selected outlier in the sequence chart. You can also click the icon for an outlier directly in the sequence chart to highlight the path to the root cause series for that outlier.

For the outlier at 2009-04-05, the root cause is Lever3. The diagram shows that Lever3 is a direct input to KPI_19, but that it also indirectly influences KPI_19 through its effect on other series that affect KPI_19. One of the configurable parameters for outlier root cause analysis is the number of causal levels to search for root causes. By default, three levels are searched. Occurrences of the root cause series are displayed up to the specified number of causal levels. In this example, Lever3 occurs at both the first causal level and the third causal level.

Each node in the highlighted path for an outlier contains a chart whose time range depends on the level at which the node occurs. For nodes in the first causal level, the range is T-1 to T-L where T is the time at which the outlier occurs and L is the number of lag terms that are included in each model. For nodes in the second causal level, the range is T-2 to T-L-1; and for the third level the range is T-3 to T-L-2. You can obtain a detailed sequence chart of these values by single-clicking the associated node.