Perform Prediction

This page is accessed via Buy Shipment > Actions > Logistics Machine Learning > Perform Prediction.

Note: The actions Load Data into Analytics, Export to Machine Learning Service, and Perform Training must be completed before running this action.

Prerequisites: Several properties must be set for this action to work. They include the URL of the Oracle Machine Learning Service and log in details.

The Perform Prediction action sends all buy shipment attributes and event attributes along with the necessary scenario information to the machine learning service. Based on the request, the machine learning service provides the Predicted Transit Time for the selected shipment. The action populates the Predicted Arrival Time on the last Dropoff stop of the shipment. Once the prediction data is received from the machine learning service, this action populates the analytics prediction tables in Transportation Intelligence.

1. Select one ore more buy shipments.
2. Select Actions > Logistics Machine Learning > Perform Prediction. The Perform Prediction action opens the Prediction Input page where the following input is required:
   1. Enter a machine learning Project ID.
   2. Select a Scenario Model Type.
      • If the machine learning project has a Model Type of Planned ETA Prediction, you see a single option of Planned Model.
      • If the machine learning project has a Model Type of Planned and Event based ETA Prediction, you see the following options: Planned Model, Event Model, or Both.
   3. Select a Scenario ID from the resulting list. This list is empty if the specified project ID has no scenarios for which the Perform Training action was completed.
   4. Click Perform Prediction.

When the prediction is in process, you see the Prediction Results page that may include the following information in the Prediction Status section:

• Project ID
• Scenario ID
• Status: ACCEPTED, PROCESSING, COMPLETED, or FAILED

When the prediction is complete, a Shipment Predictions section is added to the page and it contains:

• Shipment ID
• Event ID (depending on the Scenario Object Model Type selected): This is the tracking event ID.
• Accuracy: This is the accuracy of the model used in the prediction. Accuracy is displayed in decimals.
• Predictions: This column lists the predicted transit time of the shipment. Prediction interval provides a range of values that predicts the value of a new observation, based on your existing model. A 95% prediction interval of 9H 30M and 10H 30M tells you that future transit time for shipments with similar attributes will fall into that range 95% of the time. There is a 5% chance that a transit time will not fall into this interval.
• Prediction Low Value: Indicates the lower end of the prediction interval.
• Prediction High Value: Indicates the higher end of the prediction interval.

If the prediction failed, you see a Failure Reasons section with details of what went wrong.