create_what_if_forecast ¶

def create_what_if_forecast(
    self,
    what_if_forecast_name: "capo_forecast.types.name.Name",
    what_if_analysis_arn: "capo_forecast.types.arn.Arn",
    *,
    config_overrides: Optional[forecastClientConfig] = None,
    time_series_transformations: Optional[
        "capo_forecast.types.time_series_transformations.TimeSeriesTransformations"
    ] = None,
    time_series_replacements_data_source: Optional[
        "capo_forecast.types.time_series_replacements_data_source.TimeSeriesReplacementsDataSource"
    ] = None,
    tags: Optional["capo_forecast.types.tags.Tags"] = None,
) -> "capo_forecast.types.create_what_if_forecast_response.CreateWhatIfForecastResponse":
    """<p>A what-if forecast is a forecast that is created from a modified version of the baseline forecast. Each what-if forecast incorporates either a replacement dataset or a set of transformations to the original dataset. </p>

    Args:
        what_if_forecast_name: <p>The name of the what-if forecast. Names must be unique within each what-if analysis.</p>
        what_if_analysis_arn: <p>The Amazon Resource Name (ARN) of the what-if analysis.</p>
        time_series_transformations: <p>The transformations that are applied to the baseline time series. Each transformation contains an action and a set of conditions. An action is applied only when all conditions are met. If no conditions are provided, the action is applied to all items.</p>
        time_series_replacements_data_source: <p>The replacement time series dataset, which contains the rows that you want to change in the related time series dataset. A replacement time series does not need to contain all rows that are in the baseline related time series. Include only the rows (measure-dimension combinations) that you want to include in the what-if forecast.</p> <p>This dataset is merged with the original time series to create a transformed dataset that is used for the what-if analysis.</p> <p>This dataset should contain the items to modify (such as item_id or workforce_type), any relevant dimensions, the timestamp column, and at least one of the related time series columns. This file should not contain duplicate timestamps for the same time series.</p> <p>Timestamps and item_ids not included in this dataset are not included in the what-if analysis. </p>
        tags: <p>A list of <a href="https://docs.aws.amazon.com/forecast/latest/dg/tagging-forecast-resources.html">tags</a> to apply to the what if forecast.</p>

    Raises:
        capo_forecast.errors.invalid_input_exception.InvalidInputException: <p>We can't process the request because it includes an invalid value or a value that exceeds the valid range.</p>
        capo_forecast.errors.limit_exceeded_exception.LimitExceededException: <p>The limit on the number of resources per account has been exceeded.</p>
        capo_forecast.errors.resource_already_exists_exception.ResourceAlreadyExistsException: <p>There is already a resource with this name. Try again with a different name.</p>
        capo_forecast.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
        capo_forecast.errors.resource_not_found_exception.ResourceNotFoundException: <p>We can't find a resource with that Amazon Resource Name (ARN). Check the ARN and try again.</p>
        capo_forecast.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_forecast.types.create_what_if_forecast_request.CreateWhatIfForecastRequest]",
    ) -> OperationResponse[
        "capo_forecast.types.create_what_if_forecast_response.CreateWhatIfForecastResponse"
    ]:
        import capo_forecast._operations.amazon_forecast.create_what_if_forecast

        output, http_response = (
            capo_forecast._operations.amazon_forecast.create_what_if_forecast.create_what_if_forecast(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_forecast.types.create_what_if_forecast_request.CreateWhatIfForecastRequest",
    ) -> dict[str, Any]:
        import capo_forecast.types.create_what_if_forecast_request

        return capo_forecast.types.create_what_if_forecast_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateWhatIfForecast", redact_input=_redact_input),
    )
    input_: capo_forecast.types.create_what_if_forecast_request.CreateWhatIfForecastRequest = {
        "what_if_forecast_name": what_if_forecast_name,
        "what_if_analysis_arn": what_if_analysis_arn,
    }
    if time_series_transformations is not None:
        input_["time_series_transformations"] = time_series_transformations
    if time_series_replacements_data_source is not None:
        input_["time_series_replacements_data_source"] = (
            time_series_replacements_data_source
        )
    if tags is not None:
        input_["tags"] = tags

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output