evalml.pipelines.components.ARIMARegressor¶
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class
evalml.pipelines.components.
ARIMARegressor
(date_index=None, trend=None, start_p=2, d=0, start_q=2, max_p=5, max_d=2, max_q=5, seasonal=True, n_jobs=- 1, 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
Currently ARIMARegressor isn’t supported via conda install. It’s recommended that it be installed via PyPI.
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name
= 'ARIMA Regressor'¶
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model_family
= 'arima'¶
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supported_problem_types
= [<ProblemTypes.TIME_SERIES_REGRESSION: 'time series regression'>]¶
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hyperparameter_ranges
= {'d': Integer(low=0, high=2, prior='uniform', transform='identity'), 'max_d': Integer(low=2, high=5, prior='uniform', transform='identity'), 'max_p': Integer(low=3, high=10, prior='uniform', transform='identity'), 'max_q': Integer(low=3, high=10, prior='uniform', transform='identity'), 'seasonal': [True, False], 'start_p': Integer(low=1, high=3, prior='uniform', transform='identity'), 'start_q': Integer(low=1, high=3, prior='uniform', transform='identity')}¶
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default_parameters
= {'d': 0, 'date_index': None, 'max_d': 2, 'max_p': 5, 'max_q': 5, 'n_jobs': -1, 'seasonal': True, 'start_p': 2, 'start_q': 2, 'trend': None}¶
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predict_uses_y
= False¶
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:
- param date_index
Specifies the name of the column in X that provides the datetime objects. Defaults to None.
Constructs a new component with the same parameters and random state.
Describe a component and its parameters
Fits component to data
Loads component at file path
Make predictions using selected features.
Make probability estimates for labels.
Saves component at file path
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