verticapy.machine_learning.vertica.tsa.ensemble.TimeSeriesByCategory.fit#

TimeSeriesByCategory.fit(input_relation: str | vDataFrame, ts: str, y: str, by: str, test_relation: str | vDataFrame = '', return_report: bool = False) → str | None#

Trains the model.

Parameters#

input_relation: SQLRelation: Training relation.
ts: str: TS (Time Series) :py:class`vDataColumn` used to order the data. The :py:class`vDataColumn` type must be date (date, datetime, timestamp…) or numerical.
y: str: Response column.
by: str: Column used to represent the different categories. The number of categories will define the number of models. The by column must not have more than 50 categories.
test_relation: SQLRelation, optional: Relation used to test the model.
return_report: bool, optional: [For native models] When set to True, the model summary will be returned. Otherwise, it will be printed. In case of TimeSeriesByCategory, the report of all the models for each category are merged together.

Returns#

str: model’s summary.

Examples#

This model is built based on multiple base models. You should look at the source models to see entire examples.

ARIMA; ARMA; AR; MA;