# -*- 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"""