Class ArimaForecastingMetrics (2.22.1)

ArimaForecastingMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Model evaluation metrics for ARIMA forecasting models. .. attribute:: non_seasonal_order

Non-seasonal order.

Attributes

Name	Description
arima_fitting_metrics	`Sequence[google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics]` Arima model fitting metrics.
seasonal_periods	`Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType]` Seasonal periods. Repeated because multiple periods are supported for one time series.
has_drift	`Sequence[bool]` Whether Arima model fitted with drift or not. It is always false when d is not 1.
time_series_id	`Sequence[str]` Id to differentiate different time series for the large-scale case.
arima_single_model_forecasting_metrics	`Sequence[google.cloud.bigquery_v2.types.Model.ArimaForecastingMetrics.ArimaSingleModelForecastingMetrics]` Repeated as there can be many metric sets (one for each model) in auto-arima and the large-scale case.

builtins.object > proto.message.Message > ArimaForecastingMetrics

ArimaSingleModelForecastingMetrics(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

Model evaluation metrics for a single ARIMA forecasting model.

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__(other)

Return True if the messages are unequal, False otherwise.

__setattr__(key, value)

Set the value on the given field.

For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.