SarimaxWrapper¶

class hcrystalball.wrappers.SarimaxWrapper(order=None, seasonal_order=(0, 0, 0, 0), start_params=None, method='lbfgs', maxiter=50, suppress_warnings=False, out_of_sample_size=0, scoring='mse', scoring_args=None, trend=None, with_intercept=True, name='sarimax', conf_int=False, init_with_autoarima=False, autoarima_dict=None, always_search_model=False, clip_predictions_lower=None, clip_predictions_upper=None, hcb_verbose=True)[source]¶

Bases: hcrystalball.wrappers._base.TSModelWrapper

Wrapper for ARIMA and AutoARIMA

Search for optimal order of SARIMAX type model or instantiate one in case you provide specific order.

Parameters

name (str) – Name of the model instance, used also as column name for returned prediction.
conf_int (bool) – Whether confidence intervals should be also outputed.
init_with_autoarima (bool) – Whether you want to leverage automated search of pmdarima.arima.AutoARIMA.
autoarima_dict (dict) – If init_with_autoarima is set to True, then autoarima_dict is used for instantiation of AutoARIMA class, thus it serves as configuration of AutoARIMA search.
always_search_model (bool) – If init_with_autoarima is set to True and always_search_model is set to True, then the optimal model will be searched for during each fit call. On the other hand in most cases the desired behaviour is to search for optimal model just for first fit call and reused this already found model on subsequent fit calls (i.e. during cross validation).
clip_predictions_lower (float) – Minimal value allowed for predictions - predictions will be clipped to this value.
clip_predictions_upper (float) – Maximum value allowed for predictions - predictions will be clipped to this value.
hcb_verbose (bool) – Whtether to keep (True) or suppress (False) messages to stdout and stderr from the wrapper and 3rd party libraries during fit and predict

Methods Summary

`fit`(X, y)	Transform input data to `pmdarima.arima.ARIMA` required format and fit the model.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Transform data to required format and provide predictions.
`set_params`(**params)	Set the parameters of this estimator.

Methods Documentation

fit(X, y)[source]¶

Transform input data to pmdarima.arima.ARIMA required format and fit the model.

Parameters

X (pandas.DataFrame) – Input features.
y (array_like, (1d)) – Target vector.

Returns

Return type

self

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

predict(X)[source]¶

Transform data to required format and provide predictions.

Parameters: X (pandas.DataFrame) – Input features.
Returns: Prediction is stored in column with name being the name of the wrapper. If conf_int attribute is set to True, the returned DataFrame will have three columns, with the second and third (named ‘name’_lower and ‘name’_upper).
Return type: pandas.DataFrame

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance