evalml.pipelines.components.ARIMARegressor¶

class evalml.pipelines.components.ARIMARegressor(date_index=None, trend='n', p=1, d=0, q=0, random_seed=0, **kwargs)[source]¶

Autoregressive Integrated Moving Average Model. The three parameters (p, d, q) are the AR order, the degree of differencing, and the MA order. More information here: https://www.statsmodels.org/devel/generated/statsmodels.tsa.arima_model.ARIMA.html

supported_problem_types = [<ProblemTypes.TIME_SERIES_REGRESSION: 'time series regression'>]¶

hyperparameter_ranges = {'d': Integer(low=0, high=10, prior='uniform', transform='identity'), 'p': Integer(low=0, high=10, prior='uniform', transform='identity'), 'q': Integer(low=0, high=10, prior='uniform', transform='identity')}¶

default_parameters = {'d': 0, 'order': (1, 0, 0), 'p': 1, 'q': 0, 'trend': 'n'}¶

Instance attributes

`feature_importance`	Returns array of 0’s with a length of 1 as feature_importance is not defined for ARIMA regressor.
`needs_fitting`
`parameters`	Returns the parameters which were used to initialize the component

Methods:

`__init__`	param date_column Specifies the name of the column in X that provides the datetime objects. Defaults to None.
`clone`	Constructs a new component with the same parameters and random state.
`describe`	Describe a component and its parameters
`fit`	Fits component to data
`load`	Loads component at file path
`predict`	Make predictions using selected features.
`predict_proba`	Make probability estimates for labels.
`save`	Saves component at file path

Class Inheritance¶