Time Series

AR

tsa.AR([name, overwrite_model])

Creates a inDB Autoregressor model.

Methods:

AR.contour([nbins, chart])	Draws the model's contour plot.
AR.deploySQL([ts, y, start, npredictions, ...])	Returns the SQL code needed to deploy the model.
AR.drop()	Drops the model from the VAST DataBase.
AR.export_models(name, path[, kind])	Exports machine learning models.
AR.features_importance([idx, show, chart])	Computes the model's features importance.
AR.fit(input_relation, ts, y[, ...])	Trains the model using pure SQL.
AR.get_attributes([attr_name])	Returns the model attributes.
AR.get_match_index(x, col_list[, str_check])	Returns the matching index.
AR.get_params()	Returns the parameters of the model.
AR.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
AR.import_models(path[, schema, kind])	Imports machine learning models.
AR.plot([vdf, ts, y, start, npredictions, ...])	Draws the model.
AR.predict([vdf, ts, y, start, ...])	Predicts using the input relation.
AR.regression_report([metrics, start, ...])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
AR.report([metrics, start, npredictions, method])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
AR.score([metric, start, npredictions, method])	Computes the model score.
AR.set_params([parameters])	Sets the parameters of the model.
AR.summarize()	Summarizes the model.
AR.to_binary(path)	Exports the model to the VAST Binary format.
AR.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
AR.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes:

AR.object_type

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ARIMA

tsa.ARIMA([name, overwrite_model])

Creates a inDB ARIMA model.

Methods:

ARIMA.contour([nbins, chart])	Draws the model's contour plot.
ARIMA.deploySQL([ts, y, start, ...])	Returns the SQL code needed to deploy the model.
ARIMA.drop()	Drops the model from the VAST DataBase.
ARIMA.export_models(name, path[, kind])	Exports machine learning models.
ARIMA.features_importance([idx, show, chart])	Computes the model's features importance.
ARIMA.fit(input_relation, ts, y[, ...])	Trains the model using pure SQL.
ARIMA.get_attributes([attr_name])	Returns the model attributes.
ARIMA.get_match_index(x, col_list[, str_check])	Returns the matching index.
ARIMA.get_params()	Returns the parameters of the model.
ARIMA.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
ARIMA.import_models(path[, schema, kind])	Imports machine learning models.
ARIMA.plot([vdf, ts, y, start, ...])	Draws the model.
ARIMA.predict([vdf, ts, y, start, ...])	Predicts using the input relation.
ARIMA.regression_report([metrics, start, ...])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
ARIMA.report([metrics, start, npredictions, ...])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
ARIMA.score([metric, start, npredictions, ...])	Computes the model score.
ARIMA.set_params([parameters])	Sets the parameters of the model.
ARIMA.summarize()	Summarizes the model.
ARIMA.to_binary(path)	Exports the model to the VAST Binary format.
ARIMA.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
ARIMA.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes:

ARIMA.object_type

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VAR

tsa.VAR([name, overwrite_model])

Creates a inDB VectorAutoregressor model.

Methods:

VAR.contour([nbins, chart])	Draws the model's contour plot.
VAR.deploySQL([ts, y, start, npredictions, ...])	Returns the SQL code needed to deploy the model.
VAR.drop()	Drops the model from the VAST DataBase.
VAR.export_models(name, path[, kind])	Exports machine learning models.
VAR.features_importance([idx, show, chart])	Computes the model's features importance.
VAR.fit(input_relation, ts, y[, ...])	Trains the model using pure SQL.
VAR.get_attributes([attr_name])	Returns the model attributes.
VAR.get_match_index(x, col_list[, str_check])	Returns the matching index.
VAR.get_params()	Returns the parameters of the model.
VAR.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
VAR.import_models(path[, schema, kind])	Imports machine learning models.
VAR.plot([vdf, ts, y, start, npredictions, ...])	Draws the model.
VAR.predict([vdf, ts, y, start, ...])	Predicts using the input relation.
VAR.regression_report([metrics, start, ...])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
VAR.report([metrics, start, npredictions, ...])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
VAR.score([metric, start, npredictions, method])	Computes the model score.
VAR.set_params([parameters])	Sets the parameters of the model.
VAR.summarize()	Summarizes the model.
VAR.to_binary(path)	Exports the model to the VAST Binary format.
VAR.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
VAR.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes:

VAR.object_type