Class ARIMAPlus (2.29.0) 

ARIMAPlus(
    *,
    horizon: int = 1000,
    auto_arima: bool = True,
    auto_arima_max_order: typing.Optional[int] = None,
    auto_arima_min_order: typing.Optional[int] = None,
    data_frequency: str = "auto_frequency",
    include_drift: bool = False,
    holiday_region: typing.Optional[str] = None,
    clean_spikes_and_dips: bool = True,
    adjust_step_changes: bool = True,
    forecast_limit_lower_bound: typing.Optional[float] = None,
    forecast_limit_upper_bound: typing.Optional[float] = None,
    time_series_length_fraction: typing.Optional[float] = None,
    min_time_series_length: typing.Optional[int] = None,
    max_time_series_length: typing.Optional[int] = None,
    trend_smoothing_window_size: typing.Optional[int] = None,
    decompose_time_series: bool = True
)

Time Series ARIMA Plus model.

Properties

coef_

Inspect the coefficients of the model.

..note::

Output matches that of the ML.ARIMA_COEFFICIENTS function.
See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-arima-coefficients
for the outputs relevant to this model type.
Returns
Type Description
bigframes.dataframe.DataFrame A DataFrame with the coefficients for the model.

Methods

__repr__

__repr__()

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

detect_anomalies

detect_anomalies(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    *,
    anomaly_prob_threshold: float = 0.95
) -> bigframes.dataframe.DataFrame

Detect the anomaly data points of the input.

Returns
Type Description
bigframes.dataframe.DataFrame Detected DataFrame.

fit

fit(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    transforms=None,
    id_col: typing.Optional[
        typing.Union[
            bigframes.dataframe.DataFrame,
            bigframes.series.Series,
            pandas.core.frame.DataFrame,
            pandas.core.series.Series,
        ]
    ] = None,
)

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

Forecast time series at future horizon.

Returns
Type Description
bigframes.dataframe.DataFrame The predicted DataFrames. Which contains 2 columns: "forecast_timestamp", "id" as optional, and "forecast_value".

predict_explain

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

Explain Forecast time series at future horizon.

Returns
Type Description
bigframes.dataframe.DataFrame The predicted DataFrames.

register

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

Register the model to Vertex AI.

After register, go to the 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 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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    id_col: typing.Optional[
        typing.Union[
            bigframes.dataframe.DataFrame,
            bigframes.series.Series,
            pandas.core.frame.DataFrame,
            pandas.core.series.Series,
        ]
    ] = None,
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

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.

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.

Returns
Type Description
ARIMAPlus Saved model.