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

costexplorer_get_usage_forecast(TimePeriod, Metric, Granularity, Filter,
  PredictionIntervalLevel)

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 is 2017-01-01 and end is 2017-05-01, then the cost and usage data is retrieved from 2017-01-01 up to and including 2017-04-30 but not including 2017-05-01. 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 of MONTHLY forecasts.

The get_usage_forecast operation supports only DAILY and MONTHLY 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
)