DjR >ddlZddlmZddlmZGddeeZy)N)ABCMeta) BaseEstimatorceZdZdZej dZddZej d dZej dZ ej d dZ y) BaseARIMAzA base ARIMA classc y)zFit an ARIMA modelN)selfyXfit_argss QC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\pmdarima/base.pyfitz BaseARIMA.fitNc T|j||fi||jd||d|S)aFit an ARIMA to a vector, ``y``, of observations with an optional matrix of ``exogenous`` variables, and then generate predictions. Parameters ---------- y : array-like or iterable, shape=(n_samples,) The time-series to which to fit the ``ARIMA`` estimator. This may either be a Pandas ``Series`` object (statsmodels can internally use the dates in the index), or a numpy array. This should be a one-dimensional array of floats, and should not contain any ``np.nan`` or ``np.inf`` values. X : array-like, shape=[n_obs, n_vars], optional (default=None) An optional 2-d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an ``ARIMA`` is fit on exogenous features, it must be provided exogenous features for making predictions. n_periods : int, optional (default=10) The number of periods in the future to forecast. fit_args : dict or kwargs, optional (default=None) Any keyword args to pass to the fit method. ) n_periodsr r)rpredict)r r r rr s r fit_predictzBaseARIMA.fit_predicts66 A""t||Ai1AAArc y)z"Create forecasts on a fitted modelNr)r rr return_conf_intalphakwargss r rzBaseARIMA.predict6rrc y)zGet in-sample forecastsNr)r r startenddynamicrs r predict_in_samplezBaseARIMA.predict_in_sample;rrc y)zUpdate an ARIMA modelNr)r r r maxiterrs r updatezBaseARIMA.update?rr)N )Fg?)NN) __name__ __module__ __qualname____doc__abcabstractmethodrrrrr rrr rr sr!!BD 11 && $$rr) metaclass)r&r sklearn.baserrrrr r*s  & 4$ 4$r