reconnect moved files to git repo

venv/lib/python3.11/site-packages/pmdarima/base.py

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+# -*- coding: utf-8 -*-
+#
+# Base classes and interfaces
+
+import abc
+from abc import ABCMeta
+
+from sklearn.base import BaseEstimator
+
+# TODO: change this to base TS model if we ever hope to support more
+
+
+class BaseARIMA(BaseEstimator, metaclass=ABCMeta):
+    """A base ARIMA class"""
+
+    @abc.abstractmethod
+    def fit(self, y, X, **fit_args):
+        """Fit an ARIMA model"""
+
+    def fit_predict(self, y, X=None, n_periods=10, **fit_args):
+        """Fit 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.
+        """
+        self.fit(y, X, **fit_args)
+
+        # TODO: remove kwargs from call
+        return self.predict(n_periods=n_periods, X=X, **fit_args)
+
+    # TODO: remove kwargs from all of these
+
+    @abc.abstractmethod
+    def predict(self, n_periods, X, return_conf_int=False, alpha=0.05,
+                **kwargs):
+        """Create forecasts on a fitted model"""
+
+    @abc.abstractmethod
+    def predict_in_sample(self, X, start, end, dynamic, **kwargs):
+        """Get in-sample forecasts"""
+
+    @abc.abstractmethod
+    def update(self, y, X=None, maxiter=None, **kwargs):
+        """Update an ARIMA model"""