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ArimaSingleModelForecastingMetrics(
mapping=None, *, ignore_unknown_fields=False, **kwargs
)
Model evaluation metrics for a single ARIMA forecasting model.
Attributes |
|
---|---|
Name | Description |
non_seasonal_order |
google.cloud.bigquery_v2.types.Model.ArimaOrder
Non-seasonal order. |
arima_fitting_metrics |
google.cloud.bigquery_v2.types.Model.ArimaFittingMetrics
Arima fitting metrics. |
has_drift |
bool
Is 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. |
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.