base forecaster

Author: TonyBagnall

tsml_eval/_wip/forecasting/base.py

@@ -45,9 +45,9 @@ def __init__(self, horizon=1, window=None):
         self.horizon = horizon
         self.window = window
         self._is_fitted = False
-        super().__init__()
+        super().__init__(axis=1)
 
-    @abstract
+    @abstractmethod
     def fit(self, X):
         """Fit forecaster to series X.
 
@@ -58,7 +58,7 @@ def fit(self, X):
         """
         ...
 
-    @abstract
+    @abstractmethod
     def predict(self, X):
         """
 

tsml_eval/_wip/forecasting/ets.py

@@ -0,0 +1,12 @@
+from tsml_eval._wip.forecasting.base import BaseForecaster
+
+
+def ETSForecaster(BaseForecaster):
+
+
+    def __init__(self, horizon=1, window=None):
+        self.horizon = horizon
+        self.window = window
+        self._is_fitted = False
+        super().__init__()
+

tsml_eval/_wip/forecasting/exponential_smoothing.py

@@ -69,39 +69,6 @@ def fit_ets(y, n, x, m, error, trend, season, alpha, beta, gamma, phi, e, lik, a
     nmse : int
         The number of steps ahead to be considered for the calculation of AMSE. Determines
         the forcasting horizon.
-    y : np.ndarray
-        Time series data.
-    n : int
-        The length of the time series.
-    x : np.ndarray
-        Initial states of the ETS model. Starting values for the level, trend, and seasonal
-        components. This variable evolves during execution to store the states at each time
-        step (i.e., the state space matrix).
-    m : int
-        The period of the seasonality (e.g., for quaterly data m = 4)
-    error : int
-        The type of error model (0 -> None, 1 -> additive, 2 -> multiplicative).
-    trend : int
-        The type of trend model (0 -> None, 1 -> additive, 2 -> multiplicative).
-    season : int
-        The type of seasonality model (0 -> None, 1 -> additive, 2 -> multiplicative).
-    alpha : float
-        Smoothing parameter for the level.
-    beta : float
-        Smoothing parameter for the trend.
-    gamma : float
-        Smoothing parameter for the seasonality.
-    phi : float
-        Damping parameter.
-    e : np.ndarray
-        Residuals of the fitted model.
-    lik : np.ndarray
-        Likelihood measure.
-    amse : np.ndarray
-        Empty array for storing the Average Mean Squared Error.
-    nmse : int
-        The number of steps ahead to be considered for the calculation of AMSE. Determines
-        the forcasting horizon.
 
     Returns
     -------

tsml_eval/_wip/forecasting/window_base.py

@@ -1,7 +1,8 @@
 import numpy as np
 from tsml_eval._wip.forecasting.base import BaseForecaster
+from abc import ABC, abstractmethod
+from typing import final
 from sklearn.linear_model import LinearRegression
-
 class BaseWindowForecaster(BaseForecaster):
     def __init__(self, window, horizon=1, regressor=LinearRegression()):
         self.regressor = regressor