Forecasting with AutoML

To see an example of how to create, train, and use an AutoML time-series forecasting model for batch prediction, run the " tabular forecasting model for batch prediction" notebook in one of the following environments:

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench | View on GitHub

Forecasting models predict a sequence of values. For example, as a retailer, you might want to forecast daily demand of your products for the next 3 months so that you can appropriately stock product inventories in advance.

Workflow for creating a forecast model and making inferences

The process for creating a forecast model in Vertex AI is as follows:

Steps	Description
1. Prepare tabular training data for forecast models	Prepare your tabular training data for forecast model training.
2. Create a dataset for training forecast models	Create a new dataset and associate your prepared training data with it.
3. Train a forecast model	Train a forecast model in Vertex AI using your dataset.
4. Evaluate your model	Evaluate your newly trained forecast model for inference accuracy.
5. Get inferences for a forecast model	Request batch inferences from your forecast model.

Forecasting with AutoML doesn't support online inferences. If you want to request online inferences from your forecast model, use Tabular Workflow for Forecasting.