Forecasting with AutoML
To see an example of how to create, train, and use an AutoMLtime-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 |Openin Vertex AI Workbench |View on GitHub
Open in Colab
Open in Colab Enterprise
Openin Vertex AI Workbench
View on GitHubForecasting models predict a sequence of values. For example,as a retailer, you might want to forecast daily demand of your productsfor the next 3 months so that you can appropriately stock productinventories in advance.
Workflow for creating a forecast model and making inferences
The process for creating a forecast model in Vertex AI is asfollows:
| 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 torequest online inferences from your forecast model, useTabular Workflow for Forecasting.
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