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

Register the model to Vertex AI.

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.