Example: Item Metadata File and Schema Legacy Predictors and Item Metadata See Also

Using Item Metadata Datasets

An item metadata dataset contains categorical data that provides valuable context for the items in a target time-series dataset. Unlike related time-series datasets, item metadata datasets provide information that is static. That is, the data values remain constant over time, like an item's color or brand. Item metadata datasets are optional additions to your dataset groups. You can use an item metadata only if every item in your target time-series dataset is present in the corresponding item metadata dataset.

Item metadata might include the brand, color, model, category, place of origin, or other supplemental feature of a particular item. For example, an item metadata dataset might provide context for some of the demand data found in a target time-series dataset that represents the sales of black Amazon e-readers with 32 GB of storage. Because these characteristics don't change from day-to-day or hour-to-hour, they belong in an item metadata dataset.

Item metadata is useful for discovering and tracking descriptive patterns across your time-series data. If you include an item metadata dataset in your dataset group, Forecast can train the model to make more accurate predictions based on similarities across items. For example, you might find that virtual assistant products made by Amazon are more likely to sell out than those created by other companies, and then plan your supply chain accordingly.

Item metadata is especially useful in coldstart forecasting scenarios, in which you have no historical data with which to make predictions, but do have historical data on items with similar metadata attributes. The item metadata enables Forecast to leverage similar items to your coldstart items to produce a forecast.

When you include item metadata, Forecast creates coldstart forecasts based on similar time series, which can create a more accurate forecast. Coldstart forecasts are generated for items that are in the item metadata dataset but not in the trailing time series. First, Forecast generates forecasts for the non-coldstart items, which are items with historical data in the trailing time series. Next, for each coldstart item, its nearest neighbors are found using the item metadata dataset. Then, these nearest neighbors are used to create a coldstart forecast.

Each row in an item metadata dataset can contain up to 10 metadata fields, one of which must be an identification field to match the metadata to an item in the target time series. As with all dataset types, the values of each field are designated by a dataset schema.

Python notebooks

For a step-by-step guide on using item metadata, see Incorporating Item Metadata.

Example: Item Metadata File and Schema

The following table shows a section of a correctly configured item metadata dataset file that describes Amazon e-readers. For this example, assume that the header row represents the dataset's schema, and that each listed item is in a corresponding target time-series dataset.

`item_id`	`brand`	`model`	`color`	`waterproof`
1	amazon	paperwhite	black	yes
2	amazon	paperwhite	blue	yes
3	amazon	base_model	black	no
4	amazon	base_model	white	no
...

The following is the same information represented in CSV format.


1,amazon,paperwhite,black,yes
2,amazon,paperwhite,blue,yes
3,amazon,base_model,black,no
4,amazon,base_model,white,no
...

The following is the schema for this example dataset.


{
     "attributes": [
        {
           "AttributeName": "item_id",
           "AttributeType": "string"
        },
        {
           "AttributeName": "brand",
           "AttributeType": "string"
        },
        {
           "AttributeName": "model",
           "AttributeType": "string"
        },
        {
           "AttributeName": "color",
           "AttributeType": "string"
        },
        {
           "AttributeName": "waterproof",
           "AttributeType": "string"
        }
    ]
}

Legacy Predictors and Item Metadata

Note

To upgrade an existing predictor to AutoPredictor, see Upgrading to AutoPredictor

When using a legacy predictor, you can use item metadata when training a predictor with the CNN-QR or DeepAR+ algorithms. When using AutoML, you can provide Item metadata and Forecast will only use those time series where applicable