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New-FRCPredictor-PredictorName <String>-AlgorithmArn <String>-AutoMLOverrideStrategy <AutoMLOverrideStrategy>-EvaluationParameters_BackTestWindowOffset <Int32>-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>-InputDataConfig_DatasetGroupArn <String>-FeaturizationConfig_Featurization <Featurization[]>-FeaturizationConfig_ForecastDimension <String[]>-FeaturizationConfig_ForecastFrequency <String>-ForecastHorizon <Int32>-ForecastType <String[]>-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>-EncryptionConfig_KMSKeyArn <String>-EvaluationParameters_NumberOfBacktestWindow <Int32>-OptimizationMetric <OptimizationMetric>-PerformAutoML <Boolean>-PerformHPO <Boolean>-EncryptionConfig_RoleArn <String>-InputDataConfig_SupplementaryFeature <SupplementaryFeature[]>-Tag <Tag[]>-TrainingParameter <Hashtable>-Select <String>-PassThru <SwitchParameter>-Force <SwitchParameter>
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 Forecast to evaluate each algorithm and choose the one that minimizes the objective function
, set PerformAutoML
to true
. The objective function
is defined as the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses. For more information, see EvaluationResult.
When AutoML is enabled, the following properties are disallowed: AlgorithmArn
HPOConfig
PerformHPO
TrainingParameters
Status
of the predictor must be ACTIVE
, signifying that training has completed. To get the status, use the DescribePredictor operation. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
LatencyOptimized
AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use LatencyOptimized
.This parameter is only valid for predictors trained using AutoML. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
InvalidInputException
error. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
BackTestWindowOffset
can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.ForecastHorizon
<= BackTestWindowOffset
< 1/2 * TARGET_TIME_SERIES dataset length Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | EvaluationParameters_NumberOfBacktestWindows |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | FeaturizationConfig_Featurizations |
store_id
field. If you want the sales forecast for each item by store, you would specify store_id
as the dimension.All forecast dimensions specified in the TARGET_TIME_SERIES
dataset don't need to be specified in the CreatePredictor
request. All forecast dimensions specified in the RELATED_TIME_SERIES
dataset must be specified in the CreatePredictor
request. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | FeaturizationConfig_ForecastDimensions |
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
DataFrequency
parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length. Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
mean
. The default value is ["0.10", "0.50", "0.9"]
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ForecastTypes |
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | InputDataConfig_SupplementaryFeatures |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | HPOConfig_ParameterRanges_CategoricalParameterRanges |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | HPOConfig_ParameterRanges_ContinuousParameterRanges |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | HPOConfig_ParameterRanges_IntegerParameterRanges |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
false
. In this case, you are required to specify an algorithm.Set PerformAutoML
to true
to have Amazon Forecast perform AutoML. This is a good option if you aren't sure which algorithm is suitable for your training data. In this case, PerformHPO
must be false. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
false
. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.To override the default values, set PerformHPO
to true
and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML
must be false.The following algorithms support HPO:Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | True |
Position? | 1 |
Accept pipeline input? | True (ByValue, ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
aws:
, AWS:
, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws
as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws
do not count against your tags per resource limit.Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | Tags |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | TrainingParameters |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AK |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByValue, ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByValue, ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AWSProfilesLocation, ProfilesLocation |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | StoredCredentials, AWSProfileName |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | RegionToCall |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | SK, SecretAccessKey |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ST |
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