To specify a custom machine learning model, Select Manage from the Machine Learning icon drop-down to open the Machine Learning dialog.
Name: Forecast Pro names each of your custom machine learning model specifications and saves them in the project’s database. These named specification sets provide a convenient way to apply the same machine learning model specifications to multiple items on the Navigator. The Name drop-down allows you to select previously defined specification sets, create new ones, save the current set using a different name and delete the current set.
Description: The description field allows you to enter a description for the current model selection.
All custom machine learning models will include the same features used in the automatic univariate models (e.g. seasonality/interventions). The custom dialog allows you to include additional variables.
Events: The Events display lists all available event schedules. Check the event schedules you want machine learning to consider as features in the model. Because the event codes have no natural order (that is event codes 1 and 2 are simply two different events), Forecast Pro creates a separate feature for each event code in an event schedule. The machine learning algorithm will determine which event schedules and event codes should be used in the model.
Explanatory variables: The Explanatory variables field lists all global and item-specific explanatory variables available in the project. Check the variables that you want Forecast Pro to consider as features in your machine learning model. Do not include variables that are categorical, or unordered. If you have an unordered or categorical variable, include it as an event schedule, not an explanatory variable. Note that the machine learning algorithm will consider all checked variables, but it will only use a variable if it improves the machine learning forecasts.
Auto Extend: Check the Auto Extend checkbox if you want Forecast Pro to use expert selection to generate forecasts for any explanatory variable that does not have values provided for all periods in the forecast horizon.
Parameters:
Automatic: Check the Automatic checkbox if you want Forecast Pro to automatically select the Maximum Tree Depth and Number of Trees. The parameters are described below, in the Machine Learning Methodology.
If you uncheck the Automatic checkbox, the Maximum Tree Depth and Number of Trees spinners are operational. Consult Machine Learning Methodology for more details on the parameters described below.
Maximum Tree Depth: The tree depth is the length of the longest path to a leaf or forecast node. Use the spinner to select a maximum tree depth. Please note that as you increase the tree depth, the in-sample model fit will improve, but the forecasts may be less accurate. You should consider using out-of-sample statistics to choose an appropriate maximum tree depth.
Number of Trees: Use this spinner to choose the number of trees to include in the ensemble model. As you increase the number of trees, the in-sample model fit will improve, but the forecasts may be less accurate. You should consider using out of sample statistics to choose an appropriate number of trees.
\ML=name. Use a custom machine learning model with the specifications defined in name.