Outlier Correction 

Use

 Outliers are atypical values that cannot be explained by the forecast model. The results of the forecast can be heavily influenced by outliers.

The system is able to identify and replace outliers in the historic data. To do this, the forecast procedure calculates forecast values in the past period and compares them to the historic values. If the difference (the residual) exceeds a specific threshold value, the historical value is replaced by the ex-post-forecast value for the corresponding point in time. After this correction, the forecast calculation is performed again with the amended historic data.

You determine a Sigma factor in order to define the threshold value.

Integration

Outlier detection depends on the forecast model because the forecast value is calculated using a model and its related algorithm.

Features

In the context of the forecast function, outliers in the historic values are defined by the Sigma factor. The greater the Sigma factor the more tolerant the system of atypical values, and the fewer outliers are determined.

The Sigma factor fac controls outlier detection in the following way: An observed value y is declared to be an outlier if the difference to the forecast value e is greater than fac *s. s denotes the standard deviation of the residuals.

Activities

Activate outlier correction if you think that outliers are having an unfavorable effect on the forecast result in accordance with the forecast function definition.

Do not select outlier correction if atypical values are always to be taken into consideration by the forecast.