Forecasting Class

Reference

Definition

Namespace:: Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401

Assembly:: Az.MachineLearningServices.private.dll

Important

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Forecasting task in AutoML Table vertical.

[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ForecastingTypeConverter))]
public class Forecasting : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IForecasting, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Runtime.IValidates

[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ForecastingTypeConverter))>]
type Forecasting = class
    interface IForecasting
    interface IJsonSerializable
    interface ITableVertical
    interface IAutoMlVertical
    interface IValidates

Public Class Forecasting
Implements IForecasting, IValidates

Inheritance: Object
Forecasting

Attributes: TypeConverterAttribute

Implements: IAutoMlVertical IForecasting ITableVertical IJsonSerializable IValidates

Constructors

Forecasting()

Creates an new Forecasting instance.

Properties

CvSplitColumnName	Columns to use for CVSplit data.
FeaturizationSetting	Featurization inputs needed for AutoML job.
FeaturizationSettingBlockedTransformer	These transformers shall not be used in featurization.
FeaturizationSettingColumnNameAndType	Dictionary of column name and its type (int, float, string, datetime etc).
FeaturizationSettingDatasetLanguage	Dataset language, useful for the text data.
FeaturizationSettingEnableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.
FeaturizationSettingMode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
FeaturizationSettingTransformerParam	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
ForecastHorizonMode	[Required] Set forecast horizon value selection mode.
LimitSetting	Execution constraints for AutoMLJob.
LimitSettingEnableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
LimitSettingExitScore	Exit score for the AutoML job.
LimitSettingMaxConcurrentTrial	Maximum Concurrent iterations.
LimitSettingMaxCoresPerTrial	Max cores per iteration.
LimitSettingMaxTrial	Number of iterations.
LimitSettingTimeout	AutoML job timeout.
LimitSettingTrialTimeout	Iteration timeout.
LogVerbosity	Log verbosity for the job.
NCrossValidation	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
NCrossValidationMode	[Required] Mode for determining N-Cross validations.
PrimaryMetric	Primary metric for forecasting task.
SeasonalityMode	[Required] Seasonality mode.
SettingCountryOrRegionForHoliday	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
SettingCvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if `CVStepSize` = 3 for daily data, the origin time for each fold will be three days apart.
SettingFeatureLag	Flag for generating lags for the numeric features with 'auto' or null.
SettingFrequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
SettingShortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.
SettingTargetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
SettingTimeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
SettingTimeSeriesIdColumnName	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
SettingUseStl	Configure STL Decomposition of the time-series target column.
TargetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TargetLagMode	[Required] Set target lags mode - Auto/Custom
TargetRollingWindowSizeMode	[Required] TargetRollingWindowSiz detection mode.
TaskType	[Required] Task type for AutoMLJob.
TestData	Test data input.
TestDataDescription	Description for the input.
TestDataJobInputType	[Required] Specifies the type of job.
TestDataMode	Input Asset Delivery Mode.
TestDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TestDataUri	[Required] Input Asset URI.
TrainingData	[Required] Training data input.
TrainingDataDescription	Description for the input.
TrainingDataJobInputType	[Required] Specifies the type of job.
TrainingDataMode	Input Asset Delivery Mode.
TrainingDataUri	[Required] Input Asset URI.
TrainingSettingAllowedTrainingAlgorithm	Allowed models for forecasting task.
TrainingSettingBlockedTrainingAlgorithm	Blocked models for forecasting task.
TrainingSettingEnableDnnTraining	Enable recommendation of DNN models.
TrainingSettingEnableModelExplainability	Flag to turn on explainability on best model.
TrainingSettingEnableOnnxCompatibleModel	Flag for enabling onnx compatible models.
TrainingSettingEnableStackEnsemble	Enable stack ensemble run.
TrainingSettingEnableVoteEnsemble	Enable voting ensemble run.
TrainingSettingEnsembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
TrainingSettingStackEnsembleSettingStackMetaLearnerKWarg	Optional parameters to pass to the initializer of the meta-learner.
TrainingSettingStackEnsembleSettingStackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
TrainingSettingStackEnsembleSettingStackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.
ValidationData	Validation data inputs.
ValidationDataDescription	Description for the input.
ValidationDataJobInputType	[Required] Specifies the type of job.
ValidationDataMode	Input Asset Delivery Mode.
ValidationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
ValidationDataUri	[Required] Input Asset URI.
WeightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

Methods

DeserializeFromDictionary(IDictionary)	Deserializes a IDictionary into an instance of Forecasting.
DeserializeFromPSObject(PSObject)	Deserializes a PSObject into an instance of Forecasting.
FromJson(JsonNode)	Deserializes a JsonNode into an instance of Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IForecasting.
FromJsonString(String)	Creates a new instance of Forecasting, deserializing the content from a json string.
ToJson(JsonObject, SerializationMode)	Serializes this instance of Forecasting into a JsonNode.
ToJsonString()	Serializes this instance to a json string.
ToString()
Validate(IEventListener)	Validates that this object meets the validation criteria.

Applies to

Delen via