IForecasting Interface
Definition
Important
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[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ForecastingTypeConverter))]
public interface IForecasting : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IAutoMlVertical, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ITableVertical[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ForecastingTypeConverter))>]
type IForecasting = interface
interface IJsonSerializable
interface ITableVertical
interface IAutoMlVerticalPublic Interface IForecasting
Implements IAutoMlVertical, ITableVertical- Derived
- Attributes
- Implements
Properties
| CvSplitColumnName |
Columns to use for CVSplit data. (Inherited from ITableVertical) |
| FeaturizationSettingBlockedTransformer |
These transformers shall not be used in featurization. (Inherited from ITableVertical) |
| FeaturizationSettingColumnNameAndType |
Dictionary of column name and its type (int, float, string, datetime etc). (Inherited from ITableVertical) |
| FeaturizationSettingDatasetLanguage |
Dataset language, useful for the text data. (Inherited from ITableVertical) |
| FeaturizationSettingEnableDnnFeaturization |
Determines whether to use Dnn based featurizers for data featurization. (Inherited from ITableVertical) |
| 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. (Inherited from ITableVertical) |
| 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. (Inherited from ITableVertical) |
| ForecastHorizonMode |
[Required] Set forecast horizon value selection mode. |
| LimitSettingEnableEarlyTermination |
Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. (Inherited from ITableVertical) |
| LimitSettingExitScore |
Exit score for the AutoML job. (Inherited from ITableVertical) |
| LimitSettingMaxConcurrentTrial |
Maximum Concurrent iterations. (Inherited from ITableVertical) |
| LimitSettingMaxCoresPerTrial |
Max cores per iteration. (Inherited from ITableVertical) |
| LimitSettingMaxTrial |
Number of iterations. (Inherited from ITableVertical) |
| LimitSettingTimeout |
AutoML job timeout. (Inherited from ITableVertical) |
| LimitSettingTrialTimeout |
Iteration timeout. (Inherited from ITableVertical) |
| LogVerbosity |
Log verbosity for the job. (Inherited from IAutoMlVertical) |
| NCrossValidationMode |
[Required] Mode for determining N-Cross validations. (Inherited from ITableVertical) |
| 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 |
| 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. (Inherited from IAutoMlVertical) |
| TargetLagMode |
[Required] Set target lags mode - Auto/Custom |
| TargetRollingWindowSizeMode |
[Required] TargetRollingWindowSiz detection mode. |
| TaskType |
[Required] Task type for AutoMLJob. (Inherited from IAutoMlVertical) |
| TestDataDescription |
Description for the input. (Inherited from ITableVertical) |
| TestDataJobInputType |
[Required] Specifies the type of job. (Inherited from ITableVertical) |
| TestDataMode |
Input Asset Delivery Mode. (Inherited from ITableVertical) |
| 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. (Inherited from ITableVertical) |
| TestDataUri |
[Required] Input Asset URI. (Inherited from ITableVertical) |
| TrainingDataDescription |
Description for the input. (Inherited from IAutoMlVertical) |
| TrainingDataJobInputType |
[Required] Specifies the type of job. (Inherited from IAutoMlVertical) |
| TrainingDataMode |
Input Asset Delivery Mode. (Inherited from IAutoMlVertical) |
| TrainingDataUri |
[Required] Input Asset URI. (Inherited from IAutoMlVertical) |
| 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. |
| ValidationDataDescription |
Description for the input. (Inherited from ITableVertical) |
| ValidationDataJobInputType |
[Required] Specifies the type of job. (Inherited from ITableVertical) |
| ValidationDataMode |
Input Asset Delivery Mode. (Inherited from ITableVertical) |
| 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. (Inherited from ITableVertical) |
| ValidationDataUri |
[Required] Input Asset URI. (Inherited from ITableVertical) |
| 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. (Inherited from ITableVertical) |
Methods
| ToJson(JsonObject, SerializationMode) | (Inherited from IJsonSerializable) |