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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
,
forecast_limit_lower_bound
:
typing
.
Optional
[
float
]
=
None
,
forecast_limit_upper_bound
:
typing
.
Optional
[
float
]
=
None
,
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.
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.
Returns
Type
Description
A DataFrame with the coefficients for the model.
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.
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
,
],
transforms
=
None
,
id_col
:
typing
.
Optional
[
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
]
]
=
None
,
)
API documentation for fit
method.
get_params
get_params
(
deep
:
bool
=
True
)
-
> typing
.
Dict
[
str
,
typing
.
Any
]
Get parameters for this estimator.
Parameter
Name
Description
deep
bool, default True
Default True
. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
Type
Description
Dictionary
A dictionary of parameter names mapped to their values.
predict
predict
(
X
=
None
,
*
,
horizon
:
int
=
3
,
confidence_level
:
float
=
0.95
)
-
> bigframes
.
dataframe
.
DataFrame
Forecast time series at future horizon.
Returns
Type
Description
The predicted DataFrames. Which contains 2 columns: "forecast_timestamp", "id" as optional, and "forecast_value".
predict_explain
predict_explain
(
X
=
None
,
*
,
horizon
:
int
=
3
,
confidence_level
:
float
=
0.95
)
-
> bigframes
.
dataframe
.
DataFrame
Explain Forecast time series at future horizon.
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.
Parameter
Name
Description
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
,
],
id_col
:
typing
.
Optional
[
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
pandas
.
core
.
frame
.
DataFrame
,
pandas
.
core
.
series
.
Series
,
]
]
=
None
,
)
-
> bigframes
.
dataframe
.
DataFrame
Calculate evaluation metrics of the model.
summary
summary
(
show_all_candidate_models
:
bool
=
False
)
-
> bigframes
.
dataframe
.
DataFrame
Summary of the evaluation metrics of the time series model.
to_gbq
to_gbq
(
model_name
:
str
,
replace
:
bool
=
False
)
-
> bigframes
.
ml
.
forecasting
.
ARIMAPlus
Save the model to BigQuery.
Returns
Type
Description
ARIMAPlus
Saved model.
