amazon-forecast-service — quality + safety report

---
name: amazon-forecast-service
description: "Amazon Forecast Service API skill. Use when working with Amazon Forecast Service for root. Covers 63 endpoints."
version: 1.0.0
generator: lapsh
---

# Amazon Forecast Service
API version: 2018-06-26

## Auth
AWS SigV4

## Base URL
Not specified.

## Setup
1. Configure auth: AWS SigV4
3. POST / -- create first resource

## Endpoints

63 endpoints across 1 groups. See references/api-spec.lap for full details.

### root
| Method | Path | Description |
|--------|------|-------------|
| POST | / | Creates an Amazon Forecast predictor. Amazon Forecast creates predictors with AutoPredictor, which involves applying the optimal combination of algorithms to each time series in your datasets. You can use CreateAutoPredictor to create new predictors or upgrade/retrain existing predictors.  Creating new predictors  The following parameters are required when creating a new predictor:    PredictorName - A unique name for the predictor.    DatasetGroupArn - The ARN of the dataset group used to train the predictor.    ForecastFrequency - The granularity of your forecasts (hourly, daily, weekly, etc).    ForecastHorizon - The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.   When creating a new predictor, do not specify a value for ReferencePredictorArn.  Upgrading and retraining predictors  The following parameters are required when retraining or upgrading a predictor:    PredictorName - A unique name for the predictor.    ReferencePredictorArn - The ARN of the predictor to retrain or upgrade.   When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName. |
| POST | / | Creates an Amazon Forecast dataset. The information about the dataset that you provide helps Forecast understand how to consume the data for model training. This includes the following:     DataFrequency  - How frequently your historical time-series data is collected.     Domain  and  DatasetType  - Each dataset has an associated dataset domain and a type within the domain. Amazon Forecast provides a list of predefined domains and types within each domain. For each unique dataset domain and type within the domain, Amazon Forecast requires your data to include a minimum set of predefined fields.     Schema  - A schema specifies the fields in the dataset, including the field name and data type.   After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see Importing datasets. To get a list of all your datasets, use the ListDatasets operation. For example Forecast datasets, see the Amazon Forecast Sample GitHub repository.  The Status of a dataset must be ACTIVE before you can import training data. Use the DescribeDataset operation to get the status. |
| POST | / | Creates a dataset group, which holds a collection of related datasets. You can add datasets to the dataset group when you create the dataset group, or later by using the UpdateDatasetGroup operation. After creating a dataset group and adding datasets, you use the dataset group when you create a predictor. For more information, see Dataset groups. To get a list of all your datasets groups, use the ListDatasetGroups operation.  The Status of a dataset group must be ACTIVE before you can use the dataset group to create a predictor. To get the status, use the DescribeDatasetGroup operation. |
| POST | / | Imports your training data to an Amazon Forecast dataset. You provide the location of your training data in an Amazon Simple Storage Service (Amazon S3) bucket and the Amazon Resource Name (ARN) of the dataset that you want to import the data to. You must specify a DataSource object that includes an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data, as Amazon Forecast makes a copy of your data and processes it in an internal Amazon Web Services system. For more information, see Set up permissions. The training data must be in CSV or Parquet format. The delimiter must be a comma (,). You can specify the path to a specific file, the S3 bucket, or to a folder in the S3 bucket. For the latter two cases, Amazon Forecast imports all files up to the limit of 10,000 files. Because dataset imports are not aggregated, your most recent dataset import is the one that is used when training a predictor or generating a forecast. Make sure that your most recent dataset import contains all of the data you want to model off of, and not just the new data collected since the previous import. To get a list of all your dataset import jobs, filtered by specified criteria, use the ListDatasetImportJobs operation. |
| POST | / | Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (CreateAutoPredictor)  Creates an Amazon Forecast Explainability. Explainability helps you better understand how the attributes in your datasets impact forecast. Amazon Forecast uses a metric called Impact scores to quantify the relative impact of each attribute and determine whether they increase or decrease forecast values. To enable Forecast Explainability, your predictor must include at least one of the following: related time series, item metadata, or additional datasets like Holidays and the Weather Index. CreateExplainability accepts either a Predictor ARN or Forecast ARN. To receive aggregated Impact scores for all time series and time points in your datasets, provide a Predictor ARN. To receive Impact scores for specific time series and time points, provide a Forecast ARN.  CreateExplainability with a Predictor ARN   You can only have one Explainability resource per predictor. If you already enabled ExplainPredictor in CreateAutoPredictor, that predictor already has an Explainability resource.  The following parameters are required when providing a Predictor ARN:    ExplainabilityName - A unique name for the Explainability.    ResourceArn - The Arn of the predictor.    TimePointGranularity - Must be set to “ALL”.    TimeSeriesGranularity - Must be set to “ALL”.   Do not specify a value for the following parameters:    DataSource - Only valid when TimeSeriesGranularity is “SPECIFIC”.    Schema - Only valid when TimeSeriesGranularity is “SPECIFIC”.    StartDateTime - Only valid when TimePointGranularity is “SPECIFIC”.    EndDateTime - Only valid when TimePointGranularity is “SPECIFIC”.    CreateExplainability with a Forecast ARN   You can specify a maximum of 50 time series and 500 time points.  The following parameters are required when providing a Predictor ARN:    ExplainabilityName - A unique name for the Explainability.    ResourceArn - The Arn of the forecast.    TimePointGranularity - Either “ALL” or “SPECIFIC”.    TimeSeriesGranularity - Either “ALL” or “SPECIFIC”.   If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following:    DataSource - The S3 location of the CSV file specifying your time series.    Schema - The Schema defines the attributes and attribute types listed in the Data Source.   If you set TimePointGranularity to “SPECIFIC”, you must also provide the following:    StartDateTime - The first timestamp in the range of time points.    EndDateTime - The last timestamp in the range of time points. |
| POST | / | Exports an Explainability resource created by the CreateExplainability operation. Exported files are exported to an Amazon Simple Storage Service (Amazon S3) bucket. You must specify a DataDestination object that includes an Amazon S3 bucket and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles.  The Status of the export job must be ACTIVE before you can access the export in your Amazon S3 bucket. To get the status, use the DescribeExplainabilityExport operation. |
| POST | / | 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 CreateForecastExportJob operation. The range of the forecast is determined by the ForecastHorizon value, which you specify in the CreatePredictor 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 ListForecasts 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 DescribeForecast 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. |
| POST | / | Exports a forecast created by the CreateForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions: <ForecastExportJobName>_<ExportTimestamp>_<PartNumber> where the <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ). You must specify a DataDestination object that includes an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles. For more information, see howitworks-forecast. To get a list of all your forecast export jobs, use the ListForecastExportJobs operation.  The Status of the forecast export job must be ACTIVE before you can access the forecast in your Amazon S3 bucket. To get the status, use the DescribeForecastExportJob operation. |
| POST | / | Creates a predictor monitor resource for an existing auto predictor. Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring. |
| POST | / | This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast. To create a predictor that is compatible with all aspects of Forecast, use CreateAutoPredictor.  Creates an Amazon Forecast predictor. In the request, provide a dataset group and either specify an algorithm or let Amazon Forecast choose an algorithm for you using AutoML. If you specify an algorithm, you also can override algorithm-specific hyperparameters. Amazon Forecast uses the algorithm to train a predictor using the latest version of the datasets in the specified dataset group. You can then generate a forecast using the CreateForecast operation.  To see the evaluation metrics, use the GetAccuracyMetrics operation.  You can specify a featurization configuration to fill and aggregate the data fields in the TARGET_TIME_SERIES dataset to improve model training. For more information, see FeaturizationConfig. For RELATED_TIME_SERIES datasets, CreatePredictor verifies that the DataFrequency specified when the dataset was created matches the ForecastFrequency. TARGET_TIME_SERIES datasets don't have this restriction. Amazon Forecast also verifies the delimiter and timestamp format. For more information, see howitworks-datasets-groups. By default, predictors are trained and evaluated at the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles. You can choose custom forecast types to train and evaluate your predictor by setting the ForecastTypes.   AutoML  If you want Amazon F

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