ForecastingSettings Class

Reference

Definition

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

Assembly:: Az.MachineLearningServices.private.dll

Important

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Forecasting specific parameters.

[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ForecastingSettingsTypeConverter))]
public class ForecastingSettings : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IForecastingSettings

[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ForecastingSettingsTypeConverter))>]
type ForecastingSettings = class
    interface IForecastingSettings
    interface IJsonSerializable

Public Class ForecastingSettings
Implements IForecastingSettings

Inheritance: Object
ForecastingSettings

Attributes: TypeConverterAttribute

Implements: IForecastingSettings IJsonSerializable

Constructors

ForecastingSettings()

Creates an new ForecastingSettings instance.

Properties

CountryOrRegionForHoliday	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
CvStepSize	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.
FeatureLag	Flag for generating lags for the numeric features with 'auto' or null.
ForecastHorizonMode	[Required] Set forecast horizon value selection mode.
Frequency	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.
SeasonalityMode	[Required] Seasonality mode.
ShortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.
TargetAggregateFunction	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".
TargetLagMode	[Required] Set target lags mode - Auto/Custom
TargetRollingWindowSizeMode	[Required] TargetRollingWindowSiz detection mode.
TimeColumnName	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.
TimeSeriesIdColumnName	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.
UseStl	Configure STL Decomposition of the time-series target column.

Methods

DeserializeFromDictionary(IDictionary)	Deserializes a IDictionary into an instance of ForecastingSettings.
DeserializeFromPSObject(PSObject)	Deserializes a PSObject into an instance of ForecastingSettings.
FromJson(JsonNode)	Deserializes a JsonNode into an instance of Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IForecastingSettings.
FromJsonString(String)	Creates a new instance of ForecastingSettings, deserializing the content from a json string.
ToJson(JsonObject, SerializationMode)	Serializes this instance of ForecastingSettings into a JsonNode.
ToJsonString()	Serializes this instance to a json string.
ToString()

Applies to

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