Forecasting¶
Compose tabular data forecasting pipelines.
Components:
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Prepares the parameters for the training step. |
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Preprocesses BigQuery tables for training or prediction. |
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Validates BigQuery tables for training or prediction. |
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v1.forecasting.ForecastingPrepareDataForTrainOp(input_tables: list, preprocess_metadata: dict, model_feature_columns: list | None =
None
) Outputs ¶ Prepares the parameters for the training step.
Converts the input_tables and the output of ForecastingPreprocessingOp to the input parameters of TimeSeriesDatasetCreateOp and AutoMLForecastingTrainingJobRunOp.
- Parameters¶:
- input_tables: list¶
Serialized Json array that specifies input BigQuery tables and specs.
- preprocess_metadata: dict¶
The output of ForecastingPreprocessingOp that is a serialized dictionary with 2 fields: processed_bigquery_table_uri and column_metadata.
- model_feature_columns: list | None =
None
¶ Serialized list of column names that will be used as input feature in the training step. If None, all columns will be used in training.
- Returns¶:
ime_series_identifier_column: Unknown
Name of the column that identifies the time series.
ime_series_attribute_columns: Unknown
Serialized column names that should be used as attribute columns.
available_at_forecast_columns: Unknown
Serialized column names of columns that are available at forecast.
navailable_at_forecast_columns: Unknown
Serialized column names of columns that are unavailable at forecast.
column_transformations: Unknown
Serialized transformations to apply to the input columns.
reprocess_bq_uri: Unknown
The BigQuery table that saves the preprocessing result and will be used as training input.
arget_column: Unknown
The name of the column values of which the Model is to predict.
ime_column: Unknown
Name of the column that identifies time order in the time series.
redefined_split_column: Unknown
Name of the column that specifies an ML use of the row.
weight_column: Unknown
Name of the column that should be used as the weight column.
data_granularity_unit: Unknown
The data granularity unit.
data_granularity_count: Unknown
The number of data granularity units between data points in the training data.
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v1.forecasting.ForecastingPreprocessingOp(project: str, input_tables: list, preprocess_metadata: dsl.OutputPath(dict), preprocessing_bigquery_dataset: str | None =
''
, location: str | None ='US'
)¶ Preprocesses BigQuery tables for training or prediction.
Creates a BigQuery table for training or prediction based on the input tables. For training, a primary table is required. Optionally, you can include some attribute tables. For prediction, you need to include all the tables that were used in the training, plus a plan table.
- Parameters¶:
- project: str¶
The GCP project id that runs the pipeline.
- input_tables: list¶
Serialized Json array that specifies input BigQuery tables and specs.
- preprocessing_bigquery_dataset: str | None =
''
¶ Optional BigQuery dataset to save the preprocessing result BigQuery table. If not present, a new dataset will be created by the component.
- location: str | None =
'US'
¶ Optional location for the BigQuery data, default is US.
- Returns¶:
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v1.forecasting.ForecastingValidationOp(input_tables: list, validation_theme: str, location: str | None =
'US'
)¶ Validates BigQuery tables for training or prediction.
Validates BigQuery tables for training or prediction based on predefined requirements. For training, a primary table is required. Optionally, you can include some attribute tables. For prediction, you need to include all the tables that were used in the training, plus a plan table.
- Parameters¶:
- input_tables: list¶
Serialized Json array that specifies input BigQuery tables and specs.
- validation_theme: str¶
Theme to use for validating the BigQuery tables. Acceptable values are FORECASTING_TRAINING and FORECASTING_PREDICTION.
- location: str | None =
'US'
¶ Optional location for the BigQuery data, default is US.