Forecasting overview

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

Run in Colab | Open in Vertex AI Workbench user-managed notebooks | 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 predictions

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 to 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 prediction accuracy.
5. Get predictions for a forecast model	Request batch predictions from your forecast model.