Parameter Range Automated Machine Learning (ASCMO-DYNAMIC)

General

Modeling Method

Activate the checkboxes for the modeling methods you want to use for automated machine learning.

Automatic Input Selection

Automatically learn to base the model on only the important inputs, as a trade-off between model quality and complexity. The global settings are taken into account and each model input list is a subset of the globally activated inputs.

This option is ignored if only one global input is selected.

Random Seed

Enter a seed value for the random number generator to ensure reproducible results.

Model Setting: RNN

Activate the RNN checkbox on the General tab if you want to include this element in the machine learning process to find the best result. The more checkboxes you select, the longer it takes.

Note
For a detailed description of the parameters see Model Configurations: Recurrent Neural Network (RNN)

Activate the checkboxes of the elements to be used during the machine learning process:

Cell Type
Activation Function (tanh)
Activation Function (sigmoid)
Learn Initial States: Learns the initial state of the RNNs based on the input and output values in the first time step.
Training Loss
Data Scaling

Enter the range of minimum and maximum values for the continuous parameters to be used during the automated machine learning process:

Dropout
Recurrent Dropout
Cell Memory Size
Number of Layers
Number of Iterations
Lookback Length

Training Snippet Factor : Enter a range that will be used as a factor for the training snippets. The value is used as a factor to calculate the size of the steps between the start positions of the snippets selected for the model training. So not all possible start positions for training snippets are taken, but every (factor * Lookback-Length)-th position as start position. Larger values result in a faster model training, smaller values results in a better model.

Learning Rate Start: Enter a range for the learning rate used by the optimizer during training. Larger values result in faster training.

Learning Rate Factor : Enter a range for the factor by which the final learning rate at the end of the training should differ from the start learning rate. Final Learning Rate = Start Learning Rate * Learning Rate Factor.

Optimizer Substep Start: Enter a range that splits the training data into smaller parts for which the optimizer is called separately. Larger values result in faster training. The value 1 yields the best model if the number of iterations is increased accordingly.

Optimizer Substep Factor: Enter a range for the factor by which the optimizer substeps at the end of the training should differ from the optimizer substeps at the beginning. Final Optimizer Substeps = Start Optimizer Substeps * Optimizer Substeps Factor.

Model Setting: CNN

Activate the CNN checkbox on the General tab if you want to include this element in the machine learning process to find the best result. The more checkboxes you select, the longer it takes.

Note
For a detailed description of the parameters see Model Configurations: Convolutional Neural Network (CNN)

Layer Type

Enter the range of minimum and maximum values for the continuous parameters to be used during the automated machine learning process:

Number of Layers
Number of Filters
Kernel Size
Delation Rate Base
Leaky ReLU Slope
Dropout
Use Skip Connection

Activate the checkboxes of the elements to be used during the machine learning process:

Training Loss
Data Scaling

Enter the range of minimum and maximum values for the continuous parameters to be used during the automated machine learning process:

Number of Iterations
Snippet Length
Training Snippet Factor : Enter a range that will be used as a factor for the training snippets. The value is used as a factor to calculate the size of the steps between the start positions of the snippets selected for the model training. So not all possible start positions for training snippets are taken, but every (factor * Lookback-Length)-th position as start position. Larger values result in a faster model training, smaller values results in a better model.
Learning Rate Start: Enter a range for the learning rate used by the optimizer during training. Larger values result in faster training.
Learning Rate Factor : Enter a range for the factor by which the final learning rate at the end of the training should differ from the start learning rate. Final Learning Rate = Start Learning Rate * Learning Rate Factor.
Optimizer Substep Start: Enter a range that splits the training data into smaller parts for which the optimizer is called separately. Larger values result in faster training. The value 1 yields the best model if the number of iterations is increased accordingly.
Optimizer Substep Factor: Enter a range for the factor by which the optimizer substeps at the end of the training should differ from the optimizer substeps at the beginning. Final Optimizer Substeps = Start Optimizer Substeps * Optimizer Substeps Factor.

Model Setting: NARX

Activate the NARX checkbox on the General tab if you want to include this element in the machine learning process to find the best result. The more checkboxes you select, the longer it takes.

Note
For a detailed description of the parameters see Model Configurations: NARX Structure.

Model Type

Activate the checkboxes of the model types to be used for automated machine learning.

Enter the range of minimum and maximum values for the continuous parameters to be used during the automated machine learning process:

Number of Basis Functions
Number of Iterations
Number of Input Lags
Number of Output Lags
Input Time Lag
Output Time Lag