2019/11/22 - Amazon Forecast Service - 3 updated api methods
Changes This release adds two key updates to existing APIs. 1. Amazon Forecast can now generate forecasts in any quantile using the optional parameter forecastTypes in the CreateForecast API and 2. You can get additional details (metrics and relevant error messages) on your AutoML runs using the DescribePredictor and GetAccuracyMetrics APIs.
{'ForecastTypes': ['string']}
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, multiplied by the DataFrequency value, which you specify in the CreateDataset 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.
Note
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.
Note
The Status of the forecast must be ACTIVE before you can query or export the forecast. Use the DescribeForecast operation to get the status.
See also: AWS API Documentation
Request Syntax
client.create_forecast(
ForecastName='string',
PredictorArn='string',
ForecastTypes=[
'string',
]
)
string
[REQUIRED]
A name for the forecast.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor to use to generate the forecast.
list
The quantiles at which probabilistic forecasts are generated. You can specify up to 5 quantiles per forecast. Accepted values include 0.01 to 0.99 (increments of .01 only) and mean . The mean forecast is different from the median (0.50) when the distribution is not symmetric (e.g. Beta, Negative Binomial). The default value is ["0.1", "0.5", "0.9"] .
(string) --
dict
Response Syntax
{
'ForecastArn': 'string'
}
Response Structure
(dict) --
ForecastArn (string) --
The Amazon Resource Name (ARN) of the forecast.
{'ForecastTypes': ['string']}
Describes a forecast created using the CreateForecast operation.
In addition to listing the properties provided in the CreateForecast request, this operation lists the following properties:
DatasetGroupArn - The dataset group that provided the training data.
CreationTime
LastModificationTime
Status
Message - If an error occurred, information about the error.
See also: AWS API Documentation
Request Syntax
client.describe_forecast(
ForecastArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the forecast.
dict
Response Syntax
{
'ForecastArn': 'string',
'ForecastName': 'string',
'ForecastTypes': [
'string',
],
'PredictorArn': 'string',
'DatasetGroupArn': 'string',
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
(dict) --
ForecastArn (string) --
The forecast ARN as specified in the request.
ForecastName (string) --
The name of the forecast.
ForecastTypes (list) --
The quantiles at which proababilistic forecasts were generated.
(string) --
PredictorArn (string) --
The ARN of the predictor used to generate the forecast.
DatasetGroupArn (string) --
The ARN of the dataset group that provided the data used to train the predictor.
Status (string) --
The status of the forecast. States include:
ACTIVE
CREATE_PENDING , CREATE_IN_PROGRESS , CREATE_FAILED
DELETE_PENDING , DELETE_IN_PROGRESS , DELETE_FAILED
Note
The Status of the forecast must be ACTIVE before you can query or export the forecast.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the forecast creation task was created.
LastModificationTime (datetime) --
Initially, the same as CreationTime (status is CREATE_PENDING ). Updated when inference (creating the forecast) starts (status changed to CREATE_IN_PROGRESS ), and when inference is complete (status changed to ACTIVE ) or fails (status changed to CREATE_FAILED ).
{'PredictorExecutionDetails': {'PredictorExecutions': [{'AlgorithmArn': 'string',
'TestWindows': [{'Message': 'string',
'Status': 'string',
'TestWindowEnd': 'timestamp',
'TestWindowStart': 'timestamp'}]}]}}
Describes a predictor created using the CreatePredictor operation.
In addition to listing the properties provided in the CreatePredictor request, this operation lists the following properties:
DatasetImportJobArns - The dataset import jobs used to import training data.
AutoMLAlgorithmArns - If AutoML is performed, the algorithms that were evaluated.
CreationTime
LastModificationTime
Status
Message - If an error occurred, information about the error.
See also: AWS API Documentation
Request Syntax
client.describe_predictor(
PredictorArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor that you want information about.
dict
Response Syntax
{
'PredictorArn': 'string',
'PredictorName': 'string',
'AlgorithmArn': 'string',
'ForecastHorizon': 123,
'PerformAutoML': True|False,
'PerformHPO': True|False,
'TrainingParameters': {
'string': 'string'
},
'EvaluationParameters': {
'NumberOfBacktestWindows': 123,
'BackTestWindowOffset': 123
},
'HPOConfig': {
'ParameterRanges': {
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MaxValue': 123.0,
'MinValue': 123.0,
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'IntegerParameterRanges': [
{
'Name': 'string',
'MaxValue': 123,
'MinValue': 123,
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
]
}
},
'InputDataConfig': {
'DatasetGroupArn': 'string',
'SupplementaryFeatures': [
{
'Name': 'string',
'Value': 'string'
},
]
},
'FeaturizationConfig': {
'ForecastFrequency': 'string',
'ForecastDimensions': [
'string',
],
'Featurizations': [
{
'AttributeName': 'string',
'FeaturizationPipeline': [
{
'FeaturizationMethodName': 'filling',
'FeaturizationMethodParameters': {
'string': 'string'
}
},
]
},
]
},
'EncryptionConfig': {
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
'PredictorExecutionDetails': {
'PredictorExecutions': [
{
'AlgorithmArn': 'string',
'TestWindows': [
{
'TestWindowStart': datetime(2015, 1, 1),
'TestWindowEnd': datetime(2015, 1, 1),
'Status': 'string',
'Message': 'string'
},
]
},
]
},
'DatasetImportJobArns': [
'string',
],
'AutoMLAlgorithmArns': [
'string',
],
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
(dict) --
PredictorArn (string) --
The ARN of the predictor.
PredictorName (string) --
The name of the predictor.
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm used for model training.
ForecastHorizon (integer) --
The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
PerformAutoML (boolean) --
Whether the predictor is set to perform AutoML.
PerformHPO (boolean) --
Whether the predictor is set to perform hyperparameter optimization (HPO).
TrainingParameters (dict) --
The default training parameters or overrides selected during model training. If using the AutoML algorithm or if HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
(string) --
(string) --
EvaluationParameters (dict) --
Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.
NumberOfBacktestWindows (integer) --
The number of times to split the input data. The default is 1. Valid values are 1 through 5.
BackTestWindowOffset (integer) --
The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. 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
HPOConfig (dict) --
The hyperparameter override values for the algorithm.
ParameterRanges (dict) --
Specifies the ranges of valid values for the hyperparameters.
CategoricalParameterRanges (list) --
Specifies the tunable range for each categorical hyperparameter.
(dict) --
Specifies a categorical hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the tunable categories for the hyperparameter.
(string) --
ContinuousParameterRanges (list) --
Specifies the tunable range for each continuous hyperparameter.
(dict) --
Specifies a continuous hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
Name (string) --
The name of the hyperparameter to tune.
MaxValue (float) --
The maximum tunable value of the hyperparameter.
MinValue (float) --
The minimum tunable value of the hyperparameter.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values:
Auto
Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have values greater than 0.
ReverseLogarithmic
hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0.
For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
IntegerParameterRanges (list) --
Specifies the tunable range for each integer hyperparameter.
(dict) --
Specifies an integer hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
Name (string) --
The name of the hyperparameter to tune.
MaxValue (integer) --
The maximum tunable value of the hyperparameter.
MinValue (integer) --
The minimum tunable value of the hyperparameter.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values:
Auto
Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have values greater than 0.
ReverseLogarithmic
Not supported for IntegerParameterRange .
Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0.
For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
InputDataConfig (dict) --
Describes the dataset group that contains the data to use to train the predictor.
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group.
SupplementaryFeatures (list) --
An array of supplementary features. The only supported feature is a holiday calendar.
(dict) --
Describes a supplementary feature of a dataset group. This object is part of the InputDataConfig object.
The only supported feature is a holiday calendar. If you use the calendar, all data in the datasets should belong to the same country as the calendar. For the holiday calendar data, see the Jollyday web site.
Name (string) --
The name of the feature. This must be "holiday".
Value (string) --
One of the following 2 letter country codes:
"AU" - AUSTRALIA
"DE" - GERMANY
"JP" - JAPAN
"US" - UNITED_STATES
"UK" - UNITED_KINGDOM
FeaturizationConfig (dict) --
The featurization configuration.
ForecastFrequency (string) --
The frequency of predictions in a forecast.
Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.
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.
ForecastDimensions (list) --
An array of dimension (field) names that specify how to group the generated forecast.
For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a 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.
(string) --
Featurizations (list) --
An array of featurization (transformation) information for the fields of a dataset. Only a single featurization is supported.
(dict) --
Provides featurization (transformation) information for a dataset field. This object is part of the FeaturizationConfig object.
For example:
{
"AttributeName": "demand",
FeaturizationPipeline [ {
"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "avg", "backfill": "nan"}
} ]
}
AttributeName (string) --
The name of the schema attribute that specifies the data field to be featurized. Only the target field of the TARGET_TIME_SERIES dataset type is supported. For example, for the RETAIL domain, the target is demand , and for the CUSTOM domain, the target is target_value .
FeaturizationPipeline (list) --
An array of one FeaturizationMethod object that specifies the feature transformation method.
(dict) --
Provides information about the method that featurizes (transforms) a dataset field. The method is part of the FeaturizationPipeline of the Featurization object. If you don't specify FeaturizationMethodParameters , Amazon Forecast uses default parameters.
The following is an example of how you specify a FeaturizationMethod object.
{
"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "avg", "backfill": "nan"}
}
FeaturizationMethodName (string) --
The name of the method. The "filling" method is the only supported method.
FeaturizationMethodParameters (dict) --
The method parameters (key-value pairs). Specify these parameters to override the default values. The following list shows the parameters and their valid values. Bold signifies the default value.
aggregation : sum , avg , first , min , max
frontfill : none
middlefill : zero , nan (not a number)
backfill : zero , nan
(string) --
(string) --
EncryptionConfig (dict) --
An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
RoleArn (string) --
The ARN of the IAM role that Amazon Forecast can assume to access the AWS KMS key.
Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an InvalidInputException error.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of the KMS key.
PredictorExecutionDetails (dict) --
Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.
PredictorExecutions (list) --
An array of the backtests performed to evaluate the accuracy of the predictor against a particular algorithm. The NumberOfBacktestWindows from the object determines the number of windows in the array.
(dict) --
The algorithm used to perform a backtest and the status of those tests.
AlgorithmArn (string) --
The ARN of the algorithm used to test the predictor.
TestWindows (list) --
An array of test windows used to evaluate the algorithm. The NumberOfBacktestWindows from the object determines the number of windows in the array.
(dict) --
The status, start time, and end time of a backtest, as well as a failure reason if applicable.
TestWindowStart (datetime) --
The time at which the test began.
TestWindowEnd (datetime) --
The time at which the test ended.
Status (string) --
The status of the test. Possible status values are:
ACTIVE
CREATE_IN_PROGRESS
CREATE_FAILED
Message (string) --
If the test failed, the reason why it failed.
DatasetImportJobArns (list) --
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
(string) --
AutoMLAlgorithmArns (list) --
When PerformAutoML is specified, the ARN of the chosen algorithm.
(string) --
Status (string) --
The status of the predictor. States include:
ACTIVE
CREATE_PENDING , CREATE_IN_PROGRESS , CREATE_FAILED
DELETE_PENDING , DELETE_IN_PROGRESS , DELETE_FAILED
UPDATE_PENDING , UPDATE_IN_PROGRESS , UPDATE_FAILED
Note
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the model training task was created.
LastModificationTime (datetime) --
Initially, the same as CreationTime (when the status is CREATE_PENDING ). This value is updated when training starts (when the status changes to CREATE_IN_PROGRESS ), and when training has completed (when the status changes to ACTIVE ) or fails (when the status changes to CREATE_FAILED ).