Coherent Methods fit and predict for Time Series Models

I want to create some classes and functions for Time Series Models:

from statsmodels.tsa.ar_model import AutoReg
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima.model import ARIMA

class TimeSeriesModel():
    def __init__(self, model_name, model_config):
        self.model_name = model_name
        self.model = None
        self.model_config = model_config

        super().__init__(model_name=model_name, model_config=model_config)


@abstractmethod
def get_model(self):
    pass

@abstractmethod
def fit(self, X, y):
    pass

@abstractmethod
def predict(self, X):
    pass


def get_statsmodels_models() -> Dict[str, Any]:
    """
    Returns a dictionary, with keys being statsmodels ts estimator
    names, and values being the estimator class (to be used for model instantiation).

    Returns:
        Dictionary of statsmodels time series models.
    """

    return {"ARIMA": ARIMA, "AutoReg":AutoReg, "ExponentialSmoothing":ExponentialSmoothing, "SARIMA": SARIMA}

Now I can see a list of models that I included:

get_statsmodels_models()

Next, I want to create methods to describe the model from the list, fit and predict:

I am thinking something like this:

import statsmodels.api as sm
data = sm.datasets.sunspots.load_pandas().data['SUNACTIVITY']


ts_object = TimeSeriesModel(model_name="AutoReg",
                             model_config={"lags":1,"seasonal":True, period=11})

ts_object.fit(X=data)
ts_object.predict()

Can someone please suggest ways/references how I can initialize a model with parameters (currently, I consider just models that I imported using stats library) and implement fit and predict methods?