ForecastingSettings Class

Reference

Definition

Namespace:: Azure.ResourceManager.MachineLearning.Models

Assembly:: Azure.ResourceManager.MachineLearning.dll

Package:: Azure.ResourceManager.MachineLearning v1.2.1

Package:: Azure.ResourceManager.MachineLearning v1.2.0-beta.3

Source:: ForecastingSettings.cs

Source:: ForecastingSettings.cs

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 class ForecastingSettings : System.ClientModel.Primitives.IJsonModel<Azure.ResourceManager.MachineLearning.Models.ForecastingSettings>, System.ClientModel.Primitives.IPersistableModel<Azure.ResourceManager.MachineLearning.Models.ForecastingSettings>

public class ForecastingSettings

type ForecastingSettings = class
    interface IJsonModel<ForecastingSettings>
    interface IPersistableModel<ForecastingSettings>

type ForecastingSettings = class

Public Class ForecastingSettings
Implements IJsonModel(Of ForecastingSettings), IPersistableModel(Of ForecastingSettings)

Public Class ForecastingSettings

Inheritance: Object
ForecastingSettings

Implements: IJsonModel<ForecastingSettings> IPersistableModel<ForecastingSettings> IPersistableModel<T>

Constructors

ForecastingSettings()

Initializes a new instance of ForecastingSettings.

Properties

CountryOrRegionForHolidays	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.
FeatureLags	Flag for generating lags for the numeric features with 'auto' or null.
FeaturesUnknownAtForecastTime	The feature columns that are available for training but unknown at the time of forecast/inference. If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time.
ForecastHorizon	The desired maximum forecast horizon in units of time-series frequency. Please note ForecastHorizon is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AutoForecastHorizon and CustomForecastHorizon.
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.
Seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. Please note ForecastingSeasonality is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AutoSeasonality and CustomSeasonality.
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".
TargetLags	The number of past periods to lag from the target column. Please note TargetLags is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AutoTargetLags and CustomTargetLags.
TargetRollingWindowSize	The number of past periods used to create a rolling window average of the target column. Please note TargetRollingWindowSize is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AutoTargetRollingWindowSize and CustomTargetRollingWindowSize.
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.
TimeSeriesIdColumnNames	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.

Explicit Interface Implementations

IJsonModel<ForecastingSettings>.Create(Utf8JsonReader, ModelReaderWriterOptions)	Reads one JSON value (including objects or arrays) from the provided reader and converts it to a model.
IJsonModel<ForecastingSettings>.Write(Utf8JsonWriter, ModelReaderWriterOptions)	Writes the model to the provided Utf8JsonWriter.
IPersistableModel<ForecastingSettings>.Create(BinaryData, ModelReaderWriterOptions)	Converts the provided BinaryData into a model.
IPersistableModel<ForecastingSettings>.GetFormatFromOptions(ModelReaderWriterOptions)	Gets the data interchange format (JSON, Xml, etc) that the model uses when communicating with the service.
IPersistableModel<ForecastingSettings>.Write(ModelReaderWriterOptions)	Writes the model into a BinaryData.

Applies to

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