Get Usage Forecast
costexplorer_get_usage_forecast | R Documentation |
Retrieves a forecast for how much Amazon Web Services predicts that you will use over the forecast time period that you select, based on your past usage¶
Description¶
Retrieves a forecast for how much Amazon Web Services predicts that you will use over the forecast time period that you select, based on your past usage.
Usage¶
Arguments¶
TimePeriod
[required] The start and end dates of the period that you want to retrieve usage forecast for. The start date is included in the period, but the end date isn't included in the period. For example, if
start
is2017-01-01
andend
is2017-05-01
, then the cost and usage data is retrieved from2017-01-01
up to and including2017-04-30
but not including2017-05-01
. The start date must be equal to or later than the current date to avoid a validation error.Metric
[required] Which metric Cost Explorer uses to create your forecast.
Valid values for a
get_usage_forecast
call are the following:USAGE_QUANTITY
NORMALIZED_USAGE_AMOUNT
Granularity
[required] How granular you want the forecast to be. You can get 3 months of
DAILY
forecasts or 12 months ofMONTHLY
forecasts.The
get_usage_forecast
operation supports onlyDAILY
andMONTHLY
granularities.Filter
The filters that you want to use to filter your forecast. The
get_usage_forecast
API supports filtering by the following dimensions:AZ
INSTANCE_TYPE
LINKED_ACCOUNT
LINKED_ACCOUNT_NAME
OPERATION
PURCHASE_TYPE
REGION
SERVICE
USAGE_TYPE
USAGE_TYPE_GROUP
RECORD_TYPE
OPERATING_SYSTEM
TENANCY
SCOPE
PLATFORM
SUBSCRIPTION_ID
LEGAL_ENTITY_NAME
DEPLOYMENT_OPTION
DATABASE_ENGINE
INSTANCE_TYPE_FAMILY
BILLING_ENTITY
RESERVATION_ID
SAVINGS_PLAN_ARN
PredictionIntervalLevel
Amazon Web Services Cost Explorer always returns the mean forecast as a single point. You can request a prediction interval around the mean by specifying a confidence level. The higher the confidence level, the more confident Cost Explorer is about the actual value falling in the prediction interval. Higher confidence levels result in wider prediction intervals.
Value¶
A list with the following syntax:
list(
Total = list(
Amount = "string",
Unit = "string"
),
ForecastResultsByTime = list(
list(
TimePeriod = list(
Start = "string",
End = "string"
),
MeanValue = "string",
PredictionIntervalLowerBound = "string",
PredictionIntervalUpperBound = "string"
)
)
)
Request syntax¶
svc$get_usage_forecast(
TimePeriod = list(
Start = "string",
End = "string"
),
Metric = "BLENDED_COST"|"UNBLENDED_COST"|"AMORTIZED_COST"|"NET_UNBLENDED_COST"|"NET_AMORTIZED_COST"|"USAGE_QUANTITY"|"NORMALIZED_USAGE_AMOUNT",
Granularity = "DAILY"|"MONTHLY"|"HOURLY",
Filter = list(
Or = list(
list()
),
And = list(
list()
),
Not = list(),
Dimensions = list(
Key = "AZ"|"INSTANCE_TYPE"|"LINKED_ACCOUNT"|"LINKED_ACCOUNT_NAME"|"OPERATION"|"PURCHASE_TYPE"|"REGION"|"SERVICE"|"SERVICE_CODE"|"USAGE_TYPE"|"USAGE_TYPE_GROUP"|"RECORD_TYPE"|"OPERATING_SYSTEM"|"TENANCY"|"SCOPE"|"PLATFORM"|"SUBSCRIPTION_ID"|"LEGAL_ENTITY_NAME"|"DEPLOYMENT_OPTION"|"DATABASE_ENGINE"|"CACHE_ENGINE"|"INSTANCE_TYPE_FAMILY"|"BILLING_ENTITY"|"RESERVATION_ID"|"RESOURCE_ID"|"RIGHTSIZING_TYPE"|"SAVINGS_PLANS_TYPE"|"SAVINGS_PLAN_ARN"|"PAYMENT_OPTION"|"AGREEMENT_END_DATE_TIME_AFTER"|"AGREEMENT_END_DATE_TIME_BEFORE"|"INVOICING_ENTITY"|"ANOMALY_TOTAL_IMPACT_ABSOLUTE"|"ANOMALY_TOTAL_IMPACT_PERCENTAGE",
Values = list(
"string"
),
MatchOptions = list(
"EQUALS"|"ABSENT"|"STARTS_WITH"|"ENDS_WITH"|"CONTAINS"|"CASE_SENSITIVE"|"CASE_INSENSITIVE"|"GREATER_THAN_OR_EQUAL"
)
),
Tags = list(
Key = "string",
Values = list(
"string"
),
MatchOptions = list(
"EQUALS"|"ABSENT"|"STARTS_WITH"|"ENDS_WITH"|"CONTAINS"|"CASE_SENSITIVE"|"CASE_INSENSITIVE"|"GREATER_THAN_OR_EQUAL"
)
),
CostCategories = list(
Key = "string",
Values = list(
"string"
),
MatchOptions = list(
"EQUALS"|"ABSENT"|"STARTS_WITH"|"ENDS_WITH"|"CONTAINS"|"CASE_SENSITIVE"|"CASE_INSENSITIVE"|"GREATER_THAN_OR_EQUAL"
)
)
),
PredictionIntervalLevel = 123
)