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ARIMAPlus
()
Time Series ARIMA Plus 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
],
*
,
anomaly_prob_threshold
:
float
=
0.95
)
-
> bigframes
.
dataframe
.
DataFrame
Detect the anomaly data points of the input.
X
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
],
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.
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
whether to replace if the model already exists. Default to False.
ARIMAPlus
