- Navigation GuideYou are on a Command (operation) page with structural examples. Use the navigation breadcrumb if you would like to return to the Client landing page.
CreateAutoPredictorCommand
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
.
Example Syntax
Use a bare-bones client and the command you need to make an API call.
import { ForecastClient, CreateAutoPredictorCommand } from "@aws-sdk/client-forecast"; // ES Modules import
// const { ForecastClient, CreateAutoPredictorCommand } = require("@aws-sdk/client-forecast"); // CommonJS import
const client = new ForecastClient(config);
const input = { // CreateAutoPredictorRequest
PredictorName: "STRING_VALUE", // required
ForecastHorizon: Number("int"),
ForecastTypes: [ // ForecastTypes
"STRING_VALUE",
],
ForecastDimensions: [ // ForecastDimensions
"STRING_VALUE",
],
ForecastFrequency: "STRING_VALUE",
DataConfig: { // DataConfig
DatasetGroupArn: "STRING_VALUE", // required
AttributeConfigs: [ // AttributeConfigs
{ // AttributeConfig
AttributeName: "STRING_VALUE", // required
Transformations: { // Transformations // required
"<keys>": "STRING_VALUE",
},
},
],
AdditionalDatasets: [ // AdditionalDatasets
{ // AdditionalDataset
Name: "STRING_VALUE", // required
Configuration: { // Configuration
"<keys>": [ // Values
"STRING_VALUE",
],
},
},
],
},
EncryptionConfig: { // EncryptionConfig
RoleArn: "STRING_VALUE", // required
KMSKeyArn: "STRING_VALUE", // required
},
ReferencePredictorArn: "STRING_VALUE",
OptimizationMetric: "WAPE" || "RMSE" || "AverageWeightedQuantileLoss" || "MASE" || "MAPE",
ExplainPredictor: true || false,
Tags: [ // Tags
{ // Tag
Key: "STRING_VALUE", // required
Value: "STRING_VALUE", // required
},
],
MonitorConfig: { // MonitorConfig
MonitorName: "STRING_VALUE", // required
},
TimeAlignmentBoundary: { // TimeAlignmentBoundary
Month: "JANUARY" || "FEBRUARY" || "MARCH" || "APRIL" || "MAY" || "JUNE" || "JULY" || "AUGUST" || "SEPTEMBER" || "OCTOBER" || "NOVEMBER" || "DECEMBER",
DayOfMonth: Number("int"),
DayOfWeek: "MONDAY" || "TUESDAY" || "WEDNESDAY" || "THURSDAY" || "FRIDAY" || "SATURDAY" || "SUNDAY",
Hour: Number("int"),
},
};
const command = new CreateAutoPredictorCommand(input);
const response = await client.send(command);
// { // CreateAutoPredictorResponse
// PredictorArn: "STRING_VALUE",
// };
CreateAutoPredictorCommand Input
Parameter | Type | Description |
---|
Parameter | Type | Description |
---|---|---|
PredictorName Required | string | undefined | A unique name for the predictor |
DataConfig | DataConfig | undefined | The data configuration for your dataset group and any additional datasets. |
EncryptionConfig | EncryptionConfig | undefined | An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests. |
ExplainPredictor | boolean | undefined | Create an Explainability resource for the predictor. |
ForecastDimensions | string[] | undefined | An array of dimension (field) names that specify how to group the generated forecast. For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a |
ForecastFrequency | string | undefined | The frequency of predictions in a forecast. Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M". The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency. When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency. |
ForecastHorizon | number | undefined | The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the TARGET_TIME_SERIES dataset length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length. If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset. |
ForecastTypes | string[] | undefined | The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with |
MonitorConfig | MonitorConfig | undefined | The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor monitoring. Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring . |
OptimizationMetric | OptimizationMetric | undefined | The accuracy metric used to optimize the predictor. |
ReferencePredictorArn | string | undefined | The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a predictor. When creating a new predictor, do not specify a value for this parameter. When upgrading or retraining a predictor, only specify values for the |
Tags | Tag[] | undefined | Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive. The following restrictions apply to tags:
|
TimeAlignmentBoundary | TimeAlignmentBoundary | undefined | The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency. Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time boundary, see Specifying a Time Boundary . If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries . |
CreateAutoPredictorCommand Output
Parameter | Type | Description |
---|
Parameter | Type | Description |
---|---|---|
$metadata Required | ResponseMetadata | Metadata pertaining to this request. |
PredictorArn | string | undefined | The Amazon Resource Name (ARN) of the predictor. |
Throws
Name | Fault | Details |
---|
Name | Fault | Details |
---|---|---|
InvalidInputException | client | We can't process the request because it includes an invalid value or a value that exceeds the valid range. |
LimitExceededException | client | The limit on the number of resources per account has been exceeded. |
ResourceAlreadyExistsException | client | There is already a resource with this name. Try again with a different name. |
ResourceInUseException | client | The specified resource is in use. |
ResourceNotFoundException | client | We can't find a resource with that Amazon Resource Name (ARN). Check the ARN and try again. |
ForecastServiceException | Base exception class for all service exceptions from Forecast service. |