Class ARIMAPlus (0.5.0)

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) -> bigframes.dataframe.DataFrame

Predict the closest cluster for each sample in X.

Parameter
Name	Description
`X`	`default None` ignored, to be compatible with other APIs.

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

After register, go to 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.

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.

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.