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Use the METRICS domain for forecasting metrics, such as revenue, sales, and cash flow. It supports the following dataset types. For each dataset type, we list required and optional fields. For information on how to map the fields to columns in your training data, see Dataset Domains and Dataset Types.
Target Time Series Dataset
Type
The following fields are required:
-
metric_name(string) -
timestamp(timestamp) -
metric_value(floating-point integer) – This is thetargetfield for which Amazon Forecast generates a forecast (for example, the amount of revenue generated on a particular day).
Ideally, only these required fields should be included. Other additional time series information should be included in a related time series dataset.
Related Time Series Dataset
Type
The following fields are required:
-
metric_name(string) -
timestamp(timestamp)
In addition to the required fields, your training data can include other fields. To include other fields in the dataset, provide the fields in a schema when you create the dataset.
Item Metadata Dataset Type
The following field is required:
-
metric_name(string)
The following field is optional and might be useful in improving forecast results:
-
category(string)
In addition to the required and suggested optional fields, your training data can include other fields. To include other fields in the dataset, provide the fields in a schema when you create the dataset.
