Model Configurations: Ensemble Model

When you select the Ensemble Model modeling method Model > Configurations, the Model Properties area of the <output> tab contains the following elements. For each output there is a separate tab.

See Model Configurations (ASCMO-DYNAMIC), for a description of the Output Properties area and the button row on the bottom of the window.

Training Labels

Validation Labels

Using a validation datatset for model selection is not available.

Include Following Models

Select two or more models to combine them for a joint prediction. For example, select a LTSM + GRU + NARX model to get an ensemble model that can be more robust and improve the prediction. The Sigma plot shows the variation of the different base models.

Highlight Model Deviations

If activated the model deviation will be highlighted in red in Closed the plot .

Further settings for the highlighting are displayed:

Anomaly Percentile

Enter the percentile of reconstruction errors that are considered normal. If validation datasets are used for model selection, this value is calculated based on validation data, otherwise based on training data. The corresponding value is mapped to 0.5 in the anomaly score.

Smoothing

Enter the window size value for data points as steps for a median filter. For the prediction of an anomaly, the signals can be smoothed. This can lead to better results.

Smoothing Window Centered

Activate if you want the smoothing window to be centered around the evaluation point, i.e. future points are also taken into account. If deactivated, only past points are used for calculations in the smoothing window.

Rounding Threshold

Enter an anomaly prediction threshold below which the model prediction is automatically set to 0, and 1 otherwise. The threshold is displayed in the Receiver Operating Characteristic (Model > Anomaly Detection: Receiver Operating Characteristic).