forecastservice_list_what_if_analyses: Returns a list of what-if analyses created using the...

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

Returns a list of what-if analyses created using the CreateWhatIfAnalysis operation

Description

Returns a list of what-if analyses created using the create_what_if_analysis operation. For each what-if analysis, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the what-if analysis ARN with the describe_what_if_analysis operation.

See https://www.paws-r-sdk.com/docs/forecastservice_list_what_if_analyses/ for full documentation.

Usage

forecastservice_list_what_if_analyses(
  NextToken = NULL,
  MaxResults = NULL,
  Filters = NULL
)

Arguments

NextToken	If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
MaxResults	The number of items to return in the response.
Filters	An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if analysis jobs that match the statement from the list, respectively. The match statement consists of a key and a value. Filter properties Condition - The condition to apply. Valid values are IS and IS_NOT. To include the what-if analysis jobs that match the statement, specify IS. To exclude matching what-if analysis jobs, specify IS_NOT. Key - The name of the parameter to filter on. Valid values are WhatIfAnalysisArn and Status. Value - The value to match. For example, to list all jobs that export a forecast named electricityWhatIf, specify the following filter: ⁠"Filters": [ { "Condition": "IS", "Key": "WhatIfAnalysisArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityWhatIf" } ]⁠

paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.