Create Forecast
forecastservice_create_forecast | R Documentation |
Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was used to train the predictor¶
Description¶
Creates a forecast for each item in the TARGET_TIME_SERIES
dataset
that was used to train the predictor. This is known as inference. To
retrieve the forecast for a single item at low latency, use the
operation. To export the complete forecast into your Amazon Simple
Storage Service (Amazon S3) bucket, use the create_forecast_export_job
operation.
The range of the forecast is determined by the ForecastHorizon
value,
which you specify in the create_predictor
request. When you query a
forecast, you can request a specific date range within the forecast.
To get a list of all your forecasts, use the list_forecasts
operation.
The forecasts generated by Amazon Forecast are in the same time zone as the dataset that was used to create the predictor.
For more information, see howitworks-forecast.
The Status
of the forecast must be ACTIVE
before you can query or
export the forecast. Use the describe_forecast
operation to get the
status.
By default, a forecast includes predictions for every item (item_id
)
in the dataset group that was used to train the predictor. However, you
can use the TimeSeriesSelector
object to generate a forecast on a
subset of time series. Forecast creation is skipped for any time series
that you specify that are not in the input dataset. The forecast export
file will not contain these time series or their forecasted values.
Usage¶
forecastservice_create_forecast(ForecastName, PredictorArn,
ForecastTypes, Tags, TimeSeriesSelector)
Arguments¶
ForecastName
[required] A name for the forecast.
PredictorArn
[required] The Amazon Resource Name (ARN) of the predictor to use to generate the forecast.
ForecastTypes
The quantiles at which probabilistic forecasts are generated. You can currently specify up to 5 quantiles per forecast. Accepted values include
0.01 to 0.99
(increments of .01 only) andmean
. The mean forecast is different from the median (0.50) when the distribution is not symmetric (for example, Beta and Negative Binomial).The default quantiles are the quantiles you specified during predictor creation. If you didn't specify quantiles, the default values are
["0.1", "0.5", "0.9"]
.Tags
The optional metadata that you apply to the forecast to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use
aws:
,AWS:
, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value hasaws
as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix ofaws
do not count against your tags per resource limit.
TimeSeriesSelector
Defines the set of time series that are used to create the forecasts in a
TimeSeriesIdentifiers
object.The
TimeSeriesIdentifiers
object needs the following information:DataSource
Format
Schema
Value¶
A list with the following syntax:
Request syntax¶
svc$create_forecast(
ForecastName = "string",
PredictorArn = "string",
ForecastTypes = list(
"string"
),
Tags = list(
list(
Key = "string",
Value = "string"
)
),
TimeSeriesSelector = list(
TimeSeriesIdentifiers = list(
DataSource = list(
S3Config = list(
Path = "string",
RoleArn = "string",
KMSKeyArn = "string"
)
),
Schema = list(
Attributes = list(
list(
AttributeName = "string",
AttributeType = "string"|"integer"|"float"|"timestamp"|"geolocation"
)
)
),
Format = "string"
)
)
)