Class ARIMAPlus (2.5.0)

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.

Parameters
Name	Description
`horizon`	`int, default 1,000` The number of time points to forecast. Default to 1,000, max value 10,000.
`auto_arima`	`bool, default True` Determines whether the training process uses auto.ARIMA or not. If True, training automatically finds the best non-seasonal order (that is, the p, d, q tuple) and decides whether or not to include a linear drift term when d is 1.
`auto_arima_max_order`	`int or None, default None` The maximum value for the sum of non-seasonal p and q.
`auto_arima_min_order`	`int or None, default None` The minimum value for the sum of non-seasonal p and q.
`data_frequency`	`str, default "auto_frequency"` The data frequency of the input time series. Possible values are "auto_frequency", "per_minute", "hourly", "daily", "weekly", "monthly", "quarterly", "yearly"
`include_drift`	`bool, default False` Determines whether the model should include a linear drift term or not. The drift term is applicable when non-seasonal d is 1.
`holiday_region`	`str or None, default None` The geographical region based on which the holiday effect is applied in modeling. By default, holiday effect modeling isn't used. Possible values see https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#holiday_region.
`clean_spikes_and_dips`	`bool, default True` Determines whether or not to perform automatic spikes and dips detection and cleanup in the model training pipeline. The spikes and dips are replaced with local linear interpolated values when they're detected.
`adjust_step_changes`	`bool, default True` Determines whether or not to perform automatic step change detection and adjustment in the model training pipeline.
`forecast_limit_upper_bound`	`float or None, default None` The upper bound of the forecasting values. When you specify the `forecast_limit_upper_bound` option, all of the forecast values must be less than the specified value. For example, if you set `forecast_limit_upper_bound` to 100, then all of the forecast values are less than 100. Also, all values greater than or equal to the `forecast_limit_upper_bound` value are excluded from modelling. The forecasting limit ensures that forecasts stay within limits.
`forecast_limit_lower_bound`	`float or None, default None` The lower bound of the forecasting values where the minimum value allowed is 0. When you specify the `forecast_limit_lower_bound` option, all of the forecast values must be greater than the specified value. For example, if you set `forecast_limit_lower_bound` to 0, then all of the forecast values are larger than 0. Also, all values less than or equal to the `forecast_limit_lower_bound` value are excluded from modelling. The forecasting limit ensures that forecasts stay within limits.
`time_series_length_fraction`	`float or None, default None` The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component.
`min_time_series_length`	`int or None, default None` The minimum number of time points that are used in modeling the trend component of the time series.
`max_time_series_length`	`int or None, default None` The maximum number of time points in a time series that can be used in modeling the trend component of the time series.
`trend_smoothing_window_size`	`int or None, default None` The smoothing window size for the trend component.
`decompose_time_series`	`bool, default True` Determines whether the separate components of both the history and forecast parts of the time series (such as holiday effect and seasonal components) are saved in the 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.

Parameters
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series` Series or a DataFrame to detect anomalies.
`anomaly_prob_threshold`	`float, default 0.95` Identifies the custom threshold to use for anomaly detection. The value must be in the range [0, 1), with a default value of 0.95.

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.

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", "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.

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.

register

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

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.

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` Determine whether to replace if the model already exists. Default to False.

Returns
Type	Description
`ARIMAPlus`	Saved model.