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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
,
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
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.
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.
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.
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.
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
,
],
)
-
> bigframes
.
ml
.
base
.
_T
API documentation for fit
method.
get_params
get_params
(
deep
:
bool
=
True
)
-
> typing
.
Dict
[
str
,
typing
.
Any
]
Get parameters for this estimator.
deep
bool, default True
Default True
. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Dictionary
predict
predict
(
X
=
None
,
*
,
horizon
:
int
=
3
,
confidence_level
:
float
=
0.95
)
-
> bigframes
.
dataframe
.
DataFrame
Forecast time series at future horizon.
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).
predict_explain
predict_explain
(
X
=
None
,
*
,
horizon
:
int
=
3
,
confidence_level
:
float
=
0.95
)
-
> bigframes
.
dataframe
.
DataFrame
Explain Forecast time series at future horizon.
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).
register
register
(
vertex_ai_model_id
:
typing
.
Optional
[
str
]
=
None
)
-
> bigframes
.
ml
.
base
.
_T
Register the model to Vertex AI.
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.
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
,
],
)
-
> bigframes
.
dataframe
.
DataFrame
Calculate evaluation metrics of the model.
X
bigframes.dataframe.DataFrame
or bigframes.series.Series
or pandas.core.frame.DataFrame or pandas.core.series.Series
A BigQuery DataFrame only contains 1 column as evaluation timestamp. The timestamp must be within the horizon of the model, which by default is 1000 data points.
y
bigframes.dataframe.DataFrame
or bigframes.series.Series
or pandas.core.frame.DataFrame or pandas.core.series.Series
A BigQuery DataFrame only contains 1 column as evaluation numeric values.
summary
summary
(
show_all_candidate_models
:
bool
=
False
)
-
> bigframes
.
dataframe
.
DataFrame
Summary of the evaluation metrics of the time series model.
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.
to_gbq
to_gbq
(
model_name
:
str
,
replace
:
bool
=
False
)
-
> bigframes
.
ml
.
forecasting
.
ARIMAPlus
Save the model to BigQuery.
model_name
str
The name of the model.
replace
bool, default False
Determine whether to replace if the model already exists. Default to False.
ARIMAPlus
