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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 |
|
---|---|
Name | Description |
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, defalut 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.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
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],
*,
anomaly_prob_threshold: float = 0.95
) -> bigframes.dataframe.DataFrame
Detect the anomaly data points of the input.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.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. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
Detected DataFrame. |
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.
Parameter | |
---|---|
Name | Description |
deep |
bool, default True
Default |
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.
Parameters | |
---|---|
Name | Description |
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). |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
The predicted DataFrames. Which contains 2 columns: "forecast_timestamp" and "forecast_value". |
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],
y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame
Calculate evaluation metrics of the model.
Parameters | |
---|---|
Name | Description |
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. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
A DataFrame as evaluation result. |
summary
summary(show_all_candidate_models: bool = False) -> bigframes.dataframe.DataFrame
Summary of the evaluation metrics of the time series model.
Parameter | |
---|---|
Name | Description |
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. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame |
A DataFrame as evaluation result. |
to_gbq
to_gbq(
model_name: str, replace: bool = False
) -> bigframes.ml.forecasting.ARIMAPlus
Save the model to BigQuery.
Parameters | |
---|---|
Name | Description |
model_name |
str
The name of the model. |
replace |
bool, default False
Determine whether to replace if the model already exists. Default to False. |
Returns | |
---|---|
Type | Description |
ARIMAPlus |
Saved model. |