Class ARIMAPlus (0.21.0)

Version 0.21.0

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ARIMAPlus()

Time Series ARIMA Plus model.

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values

fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

API documentation for fit method.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
deep	bool, default True Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
Dictionary	A dictionary of parameter names mapped to their values.

predict

predict(
    X=None, horizon: int = 3, confidence_level: float = 0.95
) -> bigframes.dataframe.DataFrame

Predict the closest cluster for each sample in X.

Parameters
Name	Description
X	default None ignored, to be compatible with other APIs.
confidence_level	float, default 0.95 a float value that specifies percentage of the future values that fall in the prediction interval. The valid input range is [0.0, 1.0).

Returns
Type	Description
bigframes.dataframe.DataFrame	The predicted DataFrames. Which contains 2 columns "forecast_timestamp" and "forecast_value".

register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

After register, go to Google Cloud Console (https://console.cloud.google.com/vertex-ai/models) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.

Parameter
Name	Description
vertex_ai_model_id	Optional[str], default None optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.

score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

Note: Output matches that of the BigQuery ML.EVALUTE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#time_series_models for the outputs relevant to this model type.

Parameters
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series A BigQuery DataFrame only contains 1 column as evaluation timestamp. The timestamp must be within the horizon of the model, which by default is 1000 data points.
y	bigframes.dataframe.DataFrame or bigframes.series.Series A BigQuery DataFrame only contains 1 column as evaluation numeric values.

Returns
Type	Description
bigframes.dataframe.DataFrame	A DataFrame as evaluation result.

summary

summary(show_all_candidate_models: bool = False) -> bigframes.dataframe.DataFrame

Summary of the evaluation metrics of the time series model.

Note: Output matches that of the BigQuery ML.ARIMA_EVALUATE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-arima-evaluate for the outputs relevant to this model type.

Parameter
Name	Description
show_all_candidate_models	bool, default to False Whether to show evaluation metrics or an error message for either all candidate models or for only the best model with the lowest AIC. Default to False.

Returns
Type	Description
bigframes.dataframe.DataFrame	A DataFrame as evaluation result.

to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.forecasting.ARIMAPlus

Save the model to BigQuery.

Parameters
Name	Description
model_name	str the name of the model.
replace	bool, default False whether to replace if the model already exists. Default to False.

Returns
Type	Description
ARIMAPlus	saved model.