ForecastingSettings Class
Definition
Important
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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 ForecastingSettingstype ForecastingSettings = class
interface IJsonModel<ForecastingSettings>
interface IPersistableModel<ForecastingSettings>type ForecastingSettings = classPublic Class ForecastingSettings
Implements IJsonModel(Of ForecastingSettings), IPersistableModel(Of ForecastingSettings)Public Class ForecastingSettings- Inheritance
-
ForecastingSettings
- Implements
Constructors
| Name | Description |
|---|---|
| ForecastingSettings() |
Initializes a new instance of ForecastingSettings. |
Properties
| Name | Description |
|---|---|
| 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 |
| FeatureLags |
Flag for generating lags for the numeric features. |
| 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. |
| 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. |
| ShortSeriesHandlingConfig |
The parameter defining how if AutoML should handle short time series. |
| TargetAggregateFunction |
Target aggregate function. |
| TargetLags |
The number of past periods to lag from the target column. |
| TargetRollingWindowSize |
The number of past periods used to create a rolling window average of the target column. |
| 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. |
Methods
Explicit Interface Implementations
| Name | Description |
|---|---|
| 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. |
