Class ArimaForecastingMetrics (3.26.0) 

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

Model evaluation metrics for ARIMA forecasting models.

Attributes
Name Description
non_seasonal_order Sequence[google.cloud.bigquery_v2.types.Model.ArimaOrder]
Non-seasonal order.
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.

Classes

ArimaSingleModelForecastingMetrics

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

Model evaluation metrics for a single ARIMA forecasting model.

Methods

__delattr__

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

__setattr__

__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.