Class AutoMlForecastingInputs (3.17.0)

Protobuf type google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Additional experiment flags for the time series forcasting training.

repeated string additional_experiments = 25;

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.

repeated string available_at_forecast_columns = 21;

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.

repeated string available_at_forecast_columns = 21;

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.

repeated string available_at_forecast_columns = 21;

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.

repeated string available_at_forecast_columns = 21;

The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field.

int64 context_window = 24;

Expected difference in time granularity between rows in the data.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

Expected difference in time granularity between rows in the data.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;

The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field.

int64 forecast_horizon = 23;

Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives:

• "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
• "minimize-mae" - Minimize mean-absolute error (MAE).
• "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
• "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
• "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE).
• "minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles.

string optimization_objective = 5;

Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives:

• "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
• "minimize-mae" - Minimize mean-absolute error (MAE).
• "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
• "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
• "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE).
• "minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles.

string optimization_objective = 5;

Quantiles to use for minimize-quantile-loss optimization_objective. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.

repeated double quantiles = 16;

Quantiles to use for minimize-quantile-loss optimization_objective. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.

repeated double quantiles = 16;

Quantiles to use for minimize-quantile-loss optimization_objective. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.

repeated double quantiles = 16;

The name of the column that the model is to predict.

string target_column = 1;

The name of the column that the model is to predict.

string target_column = 1;

The name of the column that identifies time order in the time series.

string time_column = 3;

The name of the column that identifies time order in the time series.

string time_column = 3;

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.

repeated string time_series_attribute_columns = 19;

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.

repeated string time_series_attribute_columns = 19;

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.

repeated string time_series_attribute_columns = 19;

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.

repeated string time_series_attribute_columns = 19;

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

The name of the column that identifies the time series.

string time_series_identifier_column = 2;

Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.

int64 train_budget_milli_node_hours = 6;

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.

repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.

repeated string unavailable_at_forecast_columns = 20;

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.

repeated string unavailable_at_forecast_columns = 20;

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.

repeated string unavailable_at_forecast_columns = 20;

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.

repeated string unavailable_at_forecast_columns = 20;

Validation options for the data validation component. The available options are:

• "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails.
• "ignore-validation" - ignore the results of the validation and continue

string validation_options = 17;

Validation options for the data validation component. The available options are:

• "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails.
• "ignore-validation" - ignore the results of the validation and continue

string validation_options = 17;

Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.

string weight_column = 7;

Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.

string weight_column = 7;

Expected difference in time granularity between rows in the data.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;