public static interface TimeSeriesForecastingJobConfig.Builder extends SdkPojo, CopyableBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>
| Modifier and Type | Method and Description |
|---|---|
TimeSeriesForecastingJobConfig.Builder |
completionCriteria(AutoMLJobCompletionCriteria completionCriteria)
Sets the value of the CompletionCriteria property for this object.
|
default TimeSeriesForecastingJobConfig.Builder |
completionCriteria(Consumer<AutoMLJobCompletionCriteria.Builder> completionCriteria)
Sets the value of the CompletionCriteria property for this object.
|
TimeSeriesForecastingJobConfig.Builder |
featureSpecificationS3Uri(String featureSpecificationS3Uri)
A URL to the Amazon S3 data source containing additional selected features that complement the target,
itemID, timestamp, and grouped columns set in
TimeSeriesConfig. |
TimeSeriesForecastingJobConfig.Builder |
forecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
|
TimeSeriesForecastingJobConfig.Builder |
forecastHorizon(Integer forecastHorizon)
The number of time-steps that the model predicts.
|
TimeSeriesForecastingJobConfig.Builder |
forecastQuantiles(Collection<String> forecastQuantiles)
The quantiles used to train the model for forecasts at a specified quantile.
|
TimeSeriesForecastingJobConfig.Builder |
forecastQuantiles(String... forecastQuantiles)
The quantiles used to train the model for forecasts at a specified quantile.
|
default TimeSeriesForecastingJobConfig.Builder |
timeSeriesConfig(Consumer<TimeSeriesConfig.Builder> timeSeriesConfig)
The collection of components that defines the time-series.
|
TimeSeriesForecastingJobConfig.Builder |
timeSeriesConfig(TimeSeriesConfig timeSeriesConfig)
The collection of components that defines the time-series.
|
default TimeSeriesForecastingJobConfig.Builder |
transformations(Consumer<TimeSeriesTransformations.Builder> transformations)
The transformations modifying specific attributes of the time-series, such as filling strategies for missing
values.
|
TimeSeriesForecastingJobConfig.Builder |
transformations(TimeSeriesTransformations transformations)
The transformations modifying specific attributes of the time-series, such as filling strategies for missing
values.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildTimeSeriesForecastingJobConfig.Builder featureSpecificationS3Uri(String featureSpecificationS3Uri)
A URL to the Amazon S3 data source containing additional selected features that complement the target,
itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML
job V2 includes all the columns from the original dataset that are not already declared in
TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a
complement to the ones declared in TimeSeriesConfig.
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] }.
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric, categorical,
text, and datetime.
These column keys must not include any column set in TimeSeriesConfig.
featureSpecificationS3Uri - A URL to the Amazon S3 data source containing additional selected features that complement the target,
itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the
AutoML job V2 includes all the columns from the original dataset that are not already declared in
TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns
as a complement to the ones declared in TimeSeriesConfig.
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] }.
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric, categorical,
text, and datetime.
These column keys must not include any column set in TimeSeriesConfig.
TimeSeriesForecastingJobConfig.Builder completionCriteria(AutoMLJobCompletionCriteria completionCriteria)
completionCriteria - The new value for the CompletionCriteria property for this object.default TimeSeriesForecastingJobConfig.Builder completionCriteria(Consumer<AutoMLJobCompletionCriteria.Builder> completionCriteria)
AutoMLJobCompletionCriteria.Builder
avoiding the need to create one manually via AutoMLJobCompletionCriteria.builder().
When the Consumer completes, SdkBuilder.build() is called
immediately and its result is passed to completionCriteria(AutoMLJobCompletionCriteria).
completionCriteria - a consumer that will call methods on AutoMLJobCompletionCriteria.BuildercompletionCriteria(AutoMLJobCompletionCriteria)TimeSeriesForecastingJobConfig.Builder forecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min
(Minute). For example, 1D indicates every day and 15min indicates every 15 minutes.
The value of a frequency must not overlap with the next larger frequency. For example, you must use a
frequency of 1H instead of 60min.
The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
forecastFrequency - The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min
(Minute). For example, 1D indicates every day and 15min indicates every 15
minutes. The value of a frequency must not overlap with the next larger frequency. For example, you
must use a frequency of 1H instead of 60min.
The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
TimeSeriesForecastingJobConfig.Builder forecastHorizon(Integer forecastHorizon)
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
forecastHorizon - The number of time-steps that the model predicts. The forecast horizon is also called the prediction
length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the
dataset.TimeSeriesForecastingJobConfig.Builder forecastQuantiles(Collection<String> forecastQuantiles)
The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast
quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the
quantiles p10, p50, and p90 as default.
forecastQuantiles - The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles
from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five
forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML
job uses the quantiles p10, p50, and p90 as default.TimeSeriesForecastingJobConfig.Builder forecastQuantiles(String... forecastQuantiles)
The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast
quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the
quantiles p10, p50, and p90 as default.
forecastQuantiles - The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles
from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five
forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML
job uses the quantiles p10, p50, and p90 as default.TimeSeriesForecastingJobConfig.Builder transformations(TimeSeriesTransformations transformations)
The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
transformations - The transformations modifying specific attributes of the time-series, such as filling strategies for
missing values.default TimeSeriesForecastingJobConfig.Builder transformations(Consumer<TimeSeriesTransformations.Builder> transformations)
The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
This is a convenience method that creates an instance of theTimeSeriesTransformations.Builder
avoiding the need to create one manually via TimeSeriesTransformations.builder().
When the Consumer completes, SdkBuilder.build() is called immediately
and its result is passed to transformations(TimeSeriesTransformations).
transformations - a consumer that will call methods on TimeSeriesTransformations.Buildertransformations(TimeSeriesTransformations)TimeSeriesForecastingJobConfig.Builder timeSeriesConfig(TimeSeriesConfig timeSeriesConfig)
The collection of components that defines the time-series.
timeSeriesConfig - The collection of components that defines the time-series.default TimeSeriesForecastingJobConfig.Builder timeSeriesConfig(Consumer<TimeSeriesConfig.Builder> timeSeriesConfig)
The collection of components that defines the time-series.
This is a convenience method that creates an instance of theTimeSeriesConfig.Builder avoiding the
need to create one manually via TimeSeriesConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to timeSeriesConfig(TimeSeriesConfig).
timeSeriesConfig - a consumer that will call methods on TimeSeriesConfig.BuildertimeSeriesConfig(TimeSeriesConfig)Copyright © 2023. All rights reserved.