Label | Explanation | Data Type |
Input Time Series Data | The netCDF cube containing the variable that will be used to forecast to future time steps. This file must have an .nc file extension and must have been created using the Create Space Time Cube By Aggregating Points, Create Space Time Cube From Defined Locations, or Create Space Time Cube From Multidimensional Raster Layer tool. | File |
Output Model | The output folder location that will store the trained model. The trained model will be saved as a deep learning package file (.dlpk). | Folder |
Analysis Variable | The numeric variable in the dataset that will be forecasted to future time steps. | String |
Sequence Length
| The number of previous time steps that will be used when training the model. If the data contains seasonality (repeating cycles), provide the length corresponding to one season.
| Long |
Explanatory Training Variables
(Optional) | Independent variables from the data that will be used to train the model. Check the Categorical check box for any variables that represent classes or categories | Value Table |
Max Epochs (Optional) | The maximum number of epochs for which the model will be trained. The default is 20. | Long |
Number Of Time Steps to Exclude for Validation
(Optional) | The number of time steps that will be excluded for validation. For example, if a value of 14 is specified, the last 14 rows in the data frame will be used as validation data. The default is 10 percent of total timesteps. Ideally it should not be less than 5 percent of the total time steps in the input time cube.
| Long |
Model Type
(Optional) | Specifies the model architecture that will be used for training the model.
| String |
Batch Size (Optional) | The number of samples that will be processed at one time. The default is 64. Depending on the computer's GPU, this number can be changed to 8, 16, 32, 64, and so on. | Long |
Model Arguments (Optional) | Additional model arguments that will be used specific to each model. These arguments can be used to adjust the model complexity and size. See How Time Series forecasting models work to understand the model architecture, the supported model arguments, and their default values. | Value Table |
Stop training when model no longer improves
(Optional) | Specifies whether the model training will stop when validation loss does not register improvement after five consecutive epochs.
| Boolean |
Output Feature Class
(Optional) | The output feature class of all locations in the space-time cube with forecasted values stored as fields. The feature class will be created using prediction of the trained model on the validation dataset. The output displays the forecast for the final time step and contains pop-up charts showing the time series forecast on the validation set. | Feature Class |
Output Cube
(Optional) | An output space-time cube (.nc file) containing the values of the input space-time cube with the forecasted values for the corresponding validation time steps replaced. | File |
Multi-Step
(Optional) | Specifies whether a one-step or multistep approach will be used for training the multivariate time series forecasting model.
| Boolean |
Derived Output
Label | Explanation | Data Type |
Output Model File | The trained model that will be saved as a deep learning package file (.dlpk) in the output model folder. | File |