Class ArimaModelInfo (2.30.1)

Version 2.30.1

• 3.25.0 (latest)
• 3.24.0
• 3.23.1
• 3.22.0
• 3.21.0
• 3.20.1
• 3.19.0
• 3.18.0
• 3.17.2
• 3.16.0
• 3.15.0
• 3.14.1
• 3.13.0
• 3.12.0
• 3.11.4
• 3.4.0
• 3.3.6
• 3.2.0
• 3.1.0
• 3.0.1
• 2.34.4
• 2.33.0
• 2.32.0
• 2.31.0
• 2.30.1
• 2.29.0
• 2.28.1
• 2.27.1
• 2.26.0
• 2.25.2
• 2.24.1
• 2.23.3
• 2.22.1
• 2.21.0
• 2.20.0
• 2.19.0
• 2.18.0
• 2.17.0
• 2.16.1
• 2.15.0
• 2.14.0
• 2.13.1
• 2.12.0
• 2.11.0
• 2.10.0
• 2.9.0
• 2.8.0
• 2.7.0
• 2.6.2
• 2.5.0
• 2.4.0
• 2.3.1
• 2.2.0
• 2.1.0
• 2.0.0
• 1.28.2
• 1.27.2
• 1.26.1
• 1.25.0
• 1.24.0
• 1.23.1
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0

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

Arima model information.

Attributes

Name	Description
non_seasonal_order	google.cloud.bigquery_v2.types.Model.ArimaOrder Non-seasonal order.
arima_coefficients	google.cloud.bigquery_v2.types.Model.TrainingRun.IterationResult.ArimaResult.ArimaCoefficients Arima coefficients.
arima_fitting_metrics	google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics Arima fitting metrics.
has_drift	bool Whether Arima model fitted with drift or not. It is always false when d is not 1.
time_series_id	str The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
time_series_ids	Sequence[str] The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
seasonal_periods	Sequence[google.cloud.bigquery_v2.types.Model.SeasonalPeriod.SeasonalPeriodType] Seasonal periods. Repeated because multiple periods are supported for one time series.
has_holiday_effect	google.protobuf.wrappers_pb2.BoolValue If true, holiday_effect is a part of time series decomposition result.
has_spikes_and_dips	google.protobuf.wrappers_pb2.BoolValue If true, spikes_and_dips is a part of time series decomposition result.
has_step_changes	google.protobuf.wrappers_pb2.BoolValue If true, step_changes is a part of time series decomposition result.

Inheritance

builtins.object > proto.message.Message > ArimaModelInfo

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.