ArmMachineLearningModelFactory.ForecastingSettings Method
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Forecasting specific parameters.
public static Azure.ResourceManager.MachineLearning.Models.ForecastingSettings ForecastingSettings(string countryOrRegionForHolidays = default, int? cvStepSize = default, Azure.ResourceManager.MachineLearning.Models.MachineLearningFeatureLag? featureLags = default, Azure.ResourceManager.MachineLearning.Models.ForecastHorizon forecastHorizon = default, string frequency = default, Azure.ResourceManager.MachineLearning.Models.ForecastingSeasonality seasonality = default, Azure.ResourceManager.MachineLearning.Models.MachineLearningShortSeriesHandlingConfiguration? shortSeriesHandlingConfig = default, Azure.ResourceManager.MachineLearning.Models.TargetAggregationFunction? targetAggregateFunction = default, Azure.ResourceManager.MachineLearning.Models.TargetLags targetLags = default, Azure.ResourceManager.MachineLearning.Models.TargetRollingWindowSize targetRollingWindowSize = default, string timeColumnName = default, System.Collections.Generic.IEnumerable<string> timeSeriesIdColumnNames = default, Azure.ResourceManager.MachineLearning.Models.MachineLearningUseStl? useStl = default);static member ForecastingSettings : string * Nullable<int> * Nullable<Azure.ResourceManager.MachineLearning.Models.MachineLearningFeatureLag> * Azure.ResourceManager.MachineLearning.Models.ForecastHorizon * string * Azure.ResourceManager.MachineLearning.Models.ForecastingSeasonality * Nullable<Azure.ResourceManager.MachineLearning.Models.MachineLearningShortSeriesHandlingConfiguration> * Nullable<Azure.ResourceManager.MachineLearning.Models.TargetAggregationFunction> * Azure.ResourceManager.MachineLearning.Models.TargetLags * Azure.ResourceManager.MachineLearning.Models.TargetRollingWindowSize * string * seq<string> * Nullable<Azure.ResourceManager.MachineLearning.Models.MachineLearningUseStl> -> Azure.ResourceManager.MachineLearning.Models.ForecastingSettingsPublic Shared Function ForecastingSettings (Optional countryOrRegionForHolidays As String = Nothing, Optional cvStepSize As Nullable(Of Integer) = Nothing, Optional featureLags As Nullable(Of MachineLearningFeatureLag) = Nothing, Optional forecastHorizon As ForecastHorizon = Nothing, Optional frequency As String = Nothing, Optional seasonality As ForecastingSeasonality = Nothing, Optional shortSeriesHandlingConfig As Nullable(Of MachineLearningShortSeriesHandlingConfiguration) = Nothing, Optional targetAggregateFunction As Nullable(Of TargetAggregationFunction) = Nothing, Optional targetLags As TargetLags = Nothing, Optional targetRollingWindowSize As TargetRollingWindowSize = Nothing, Optional timeColumnName As String = Nothing, Optional timeSeriesIdColumnNames As IEnumerable(Of String) = Nothing, Optional useStl As Nullable(Of MachineLearningUseStl) = Nothing) As ForecastingSettingsParameters
- countryOrRegionForHolidays
- String
Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
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.
- featureLags
- Nullable<MachineLearningFeatureLag>
Flag for generating lags for the numeric features.
- forecastHorizon
- ForecastHorizon
The desired maximum forecast horizon in units of time-series frequency.
- frequency
- String
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.
- seasonality
- ForecastingSeasonality
Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- shortSeriesHandlingConfig
- Nullable<MachineLearningShortSeriesHandlingConfiguration>
The parameter defining how if AutoML should handle short time series.
- targetAggregateFunction
- Nullable<TargetAggregationFunction>
Target aggregate function.
- targetLags
- TargetLags
The number of past periods to lag from the target column.
- targetRollingWindowSize
- TargetRollingWindowSize
The number of past periods used to create a rolling window average of the target column.
- timeColumnName
- String
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.
- timeSeriesIdColumnNames
- IEnumerable<String>
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
- Nullable<MachineLearningUseStl>
Configure STL Decomposition of the time-series target column.
Returns
A new ForecastingSettings instance for mocking.
