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ARIMAPlus
()
Time Series ARIMA Plus model.
Methods
__repr__
__repr__
()
Print the estimator's constructor with all non-default parameter values
fit
fit
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
],
y
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
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
Predict the closest cluster for each sample in X.
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 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 by 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
],
y
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
],
)
-
> bigframes
.
dataframe
.
DataFrame
Calculate evaluation metrics of the model.
X
bigframes.dataframe.DataFrame
or bigframes.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
A BigQuery DataFrame only contains 1 column as evaluation numeric values.
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
whether to replace if the model already exists. Default to False.
ARIMAPlus
