arima_regressor¶
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.
Module Contents¶
Classes Summary¶
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. |
Contents¶
-
class
evalml.pipelines.components.estimators.regressors.arima_regressor.
ARIMARegressor
(time_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, maxiter=10, **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.
- Parameters
time_index (str) – Specifies the name of the column in X that provides the datetime objects. Defaults to None.
trend (str) – Controls the deterministic trend. Options are [‘n’, ‘c’, ‘t’, ‘ct’] where ‘c’ is a constant term, ‘t’ indicates a linear trend, and ‘ct’ is both. Can also be an iterable when defining a polynomial, such as [1, 1, 0, 1].
start_p (int) – Minimum Autoregressive order. Defaults to 2.
d (int) – Minimum Differencing degree. Defaults to 0.
start_q (int) – Minimum Moving Average order. Defaults to 2.
max_p (int) – Maximum Autoregressive order. Defaults to 5.
max_d (int) – Maximum Differencing degree. Defaults to 2.
max_q (int) – Maximum Moving Average order. Defaults to 5.
seasonal (boolean) – Whether to fit a seasonal model to ARIMA. Defaults to True.
n_jobs (int or None) – Non-negative integer describing level of parallelism used for pipelines. Defaults to -1.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “start_p”: Integer(1, 3), “d”: Integer(0, 2), “start_q”: Integer(1, 3), “max_p”: Integer(3, 10), “max_d”: Integer(2, 5), “max_q”: Integer(3, 10), “seasonal”: [True, False],}
model_family
ModelFamily.ARIMA
modifies_features
True
modifies_target
False
name
ARIMA Regressor
supported_problem_types
[ProblemTypes.TIME_SERIES_REGRESSION]
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Returns array of 0’s with a length of 1 as feature_importance is not defined for ARIMA regressor.
Fits ARIMA regressor to data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Make predictions using fitted ARIMA regressor.
Make probability estimates for labels.
Saves component at file path.
-
clone
(self)¶ Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
-
default_parameters
(cls)¶ Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
-
describe
(self, print_name=False, return_dict=False)¶ Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
-
property
feature_importance
(self)¶ Returns array of 0’s with a length of 1 as feature_importance is not defined for ARIMA regressor.
-
fit
(self, X, y=None)[source]¶ Fits ARIMA regressor to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If X was passed to fit but not passed in predict.
-
static
load
(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
-
needs_fitting
(self)¶ Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
-
property
parameters
(self)¶ Returns the parameters which were used to initialize the component.
-
predict
(self, X, y=None)[source]¶ Make predictions using fitted ARIMA regressor.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.Series) – Target data.
- Returns
Predicted values.
- Return type
pd.Series
- Raises
ValueError – If X was passed to fit but not passed in predict.
-
predict_proba
(self, X)¶ Make probability estimates for labels.
- Parameters
X (pd.DataFrame) – Features.
- Returns
Probability estimates.
- Return type
pd.Series
- Raises
MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.
-
save
(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)¶ Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.