Creating forecasting models using Redshift ML
Currently, if you have to perform forecasting in your data warehouse, you need to export the dataset into external systems and then apply forecasting algorithms to create output datasets and then import them back into the data warehouse for your presentation layer or further analysis. With Redshift ML’s integration with Amazon Forecast, you don’t have to perform all these steps. You can now create the forecasting models right on your dataset within your data warehouse.
In Chapter 5, we talked about the basic CREATE MODEL syntax and its constructs. Let’s take a look at the CREATE MODEL syntax for forecasting:
CREATE MODEL forecast_model_name
FROM { table_name | ( select_query ) }
TARGET column_name
IAM_ROLE { default | 'arn:aws:iam::<AWS account-id>:role/<role-name>' }
AUTO ON MODEL_TYPE FORECAST
[ OBJECTIVE optimization_metric ]
SETTINGS (S3_BUCKET 'bucket',
&...
