Importing Datasets

Datasets contain the data used to train a predictor. You create one or more Amazon Forecast datasets and import your training data into them. A dataset group is a collection of complementary datasets that detail a set of changing parameters over a series of time. After creating a dataset group, you use it to train a predictor.

Each dataset group can have up to three datasets, one of each dataset type: target time series, related time series, and item metadata.

To create and manage Forecast datasets and dataset groups, you can use the Forecast console, AWS Command Line Interface (AWS CLI), or AWS SDK.

For example Forecast datasets, see the Amazon Forecast Sample GitHub repository.

Topics

• Datasets
• Dataset Groups
• Resolving Conflicts in Data Collection Frequency
• Using Related Time Series Datasets
• Using Item Metadata Datasets
• Predefined Dataset Domains and Dataset Types
• Updating Data
• Handling Missing Values
• Dataset Guidelines for Forecast

Datasets

To create and manage Forecast datasets, you can use the Forecast APIs, including the CreateDataset and DescribeDataset operations. For a complete list of Forecast APIs, see API Reference.

When creating a dataset, you provide information, such as the following:

• The frequency/interval at which you recorded your data. For example, you might aggregate and record retail item sales every week. In the Getting Started exercise, you use the average electricity used per hour.

• The prediction format (the domain) and dataset type (within the domain). A dataset domain specifies which type of forecast you'd like to perform, while a dataset type helps you organize your training data into Forecast-friendly categories.

• The dataset schema. A schema maps the column headers of your dataset. For instance, when monitoring demand, you might have collected hourly data on the sales of an item at multiple stores. In this case, your schema would define the order, from left to right, in which timestamp, location, and hourly sales appear in your training data file. Schemas also define each column's data type, such as string or integer.

• Geolocation and time zone information. The geolocation attribute is defined within the schema with the attribute type geolocation. Time zone information is defined with the CreateDatasetImportJob operation. Both geolocation and time zone data must be included to enable the Weather Index.

Each column in your Forecast dataset represents either a forecast dimension or feature. Forecast dimensions describe the aspects of your data that do not change over time, such a store or location. Forecast features include any parameters in your data that vary across time, such as price or promotion. Some dimensions, like timestamp or itemId, are required in target time series and related time series datasets.

Dataset Domains and Dataset Types

When you create a Forecast dataset, you choose a domain and a dataset type. Forecast provides domains for a number of use cases, such as forecasting retail demand or web traffic. You can also create a custom domain. For a complete list of Forecast domains, see Predefined Dataset Domains and Dataset Types.

Within each domain, Forecast users can specify the following types of datasets:

• Target time series dataset (required) – Use this dataset type when your training data is a time series and it includes the field that you want to generate a forecast for. This field is called the target field.

• Related time series dataset (optional) – Choose this dataset type when your training data is a time series, but it doesn't include the target field. For instance, if you're forecasting item demand, a related time series dataset might have price as a field, but not demand.

• Item metadata dataset (optional) – Choose this dataset type when your training data isn't time-series data, but includes metadata information about the items in the target time series or related time series datasets. For instance, if you're forecasting item demand, an item metadata dataset might have color or brand as dimensions.

  Forecast only considers the data provided by an item metadata dataset type when you use the CNN-QR or DeepAR+ algorithm.

  Item metadata is especially useful in coldstart forecasting scenarios, in which you have little direct historical data with which to make predictions, but do have historical data on items with similar metadata attributes. When you include item metadata, Forecast creates coldstart forecasts based on similar time series, which can create a more accurate forecast.

Depending on the information in your training data and what you want to forecast, you might create more than one dataset.

For example, suppose that you want to generate a forecast for the demand of retail items, such as shoes and socks. You might create the following datasets in the RETAIL domain:

• Target time series dataset – Includes the historical time-series demand data for the retail items (item_id, timestamp, and the target field demand). Because it designates the target field that you want to forecast, you must have at least one target time series dataset in a dataset group.

  You can also add up to ten other dimensions to a target time series dataset. If you include only a target time series dataset in your dataset group, you can create forecasts at either the item level or the forecast dimension level of granularity only. For more information, see CreatePredictor.

• Related time series dataset – Includes historical time-series data other than the target field, such as price or revenue. Because related time series data must be mappable to target time series data, each related time series dataset must contain the same identifying fields. In the RETAIL domain, these would be item_id and timestamp.

  A related time series dataset might contain data that refines the forecasts made off of your target time series dataset. For example, you might include price data in your related time series dataset on the future dates that you want to generate a forecast for. This way, Forecast can make predictions with an additional dimension of context. For more information, see Using Related Time Series Datasets.

• Item metadata dataset – Includes metadata for the retail items. Examples of metadata include brand, category,color, and genre.

Example Dataset with a Forecast Dimension

Continuing with the preceding example, imagine that you want to forecast the demand for shoes and socks based on a store's previous sales. In the following target time series dataset, store is a time-series forecast dimension, while demand is the target field. Socks are sold in two store locations (NYC and SFO), and shoes are sold only in ORD.

The first three rows of this table contain the first available sales data for the NYC, SFO, and ORD stores. The last three rows contain the last recorded sales data for each store. The ... row represents all of the item sales data recorded between the first and last entries.

timestamp	item_id	store	demand
2019-01-01	socks	NYC	25
2019-01-05	socks	SFO	45
2019-02-01	shoes	ORD	10
...
2019-06-01	socks	NYC	100
2019-06-05	socks	SFO	5
2019-07-01	shoes	ORD	50

Dataset Schema

Each dataset requires a schema, a user-provided JSON mapping of the fields in your training data. This is where you list both the required and optional dimensions and features that you want to include in your dataset.

If your dataset includes a geolocation attribute, define the attribute within the schema with the attribute type geolocation. For more information, see Adding Geolocation information. In order to apply the Weather Index, you must include a geolocation attribute in your target time series and any related time series datasets.

Some domains have optional dimensions that we recommend including. Optional dimensions are listed in the descriptions of each domain later in this guide. For an example, see RETAIL Domain. All optional dimensions take the data type string.

A schema is required for every dataset. The following is the accompanying schema for the example target time series dataset above.

{
     "attributes": [
        {
           "AttributeName": "timestamp",
           "AttributeType": "timestamp"
        },
        {
           "AttributeName": "item_id",
           "AttributeType": "string"
        },
        {
           "AttributeName": "store",
           "AttributeType": "string"
        },
        {
           "AttributeName": "demand",
           "AttributeType": "float"
        }
    ]
}

When you upload your training data to the dataset that uses this schema, Forecast assumes that the timestamp field is column 1, the item_id field is column 2, the store field is column 3, and the demand field, the target field, is column 4.

For the related time series dataset type, all related features must have a float or integer attribute type. For the item metadata dataset type, all features must have a string attribute type. For more information, see SchemaAttribute.

Note

An attributeName and attributeType pair is required for every column in the dataset. Forecast reserves a number of names that can't be used as the name of a schema attribute. For the list of reserved names, see Reserved Field Names.

Dataset Groups

A dataset group is a collection of one to three complimentary datasets, one of each dataset type. You import datasets to a dataset group, then use the dataset group to train a predictor.

Forecast includes the following operations to create dataset groups and add datasets to them:

• CreateDatasetGroup

• UpdateDatasetGroup

Resolving Conflicts in Data Collection Frequency

Forecast can train predictors with data that doesn't align with the data frequency you specify in the CreateDataset operation. For example, you can import data in recorded in hourly intervals even though some of the data isn't timestamped at the top of the hour (02:20, 02:45). Forecast uses the data frequency you specify to learn about your data. Then Forecast aggregates the data during predictor training. For more information see Data aggregation for different forecast frequencies.