Refactor/fit args

darts/models/arima.py

@@ -11,7 +11,6 @@
 .. [1] https://wikipedia.org/wiki/Autoregressive_integrated_moving_average
 """
 
-from typing import Optional
 from statsmodels.tsa.arima_model import ARMA as staARMA
 from statsmodels.tsa.arima_model import ARIMA as staARIMA
 from pmdarima import AutoARIMA as PmdAutoARIMA
@@ -45,8 +44,8 @@ def __init__(self, p: int = 12, d: int = 1, q: int = 0):
     def __str__(self):
         return 'ARIMA({},{},{})'.format(self.p, self.d, self.q)
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         series = self.training_series
         m = staARIMA(series.values(),
                      order=(self.p, self.d, self.q)) if self.d > 0 else staARMA(series.values(), order=(self.p, self.q))
@@ -90,8 +89,8 @@ def __init__(self, *autoarima_args, **autoarima_kwargs):
     def __str__(self):
         return 'Auto-ARIMA'
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         series = self.training_series
         self.model.fit(series.values())
 

darts/models/baselines.py

@@ -5,7 +5,6 @@
 A collection of simple benchmark models.
 """
 
-from typing import Optional
 import numpy as np
 
 from .forecasting_model import UnivariateForecastingModel
@@ -28,8 +27,8 @@ def __init__(self):
     def __str__(self):
         return 'Naive mean predictor model'
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         self.mean_val = np.mean(series.univariate_values())
 
     def predict(self, n: int):
@@ -62,8 +61,8 @@ def min_train_series_length(self):
     def __str__(self):
         return 'Naive seasonal model, with K={}'.format(self.K)
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         raise_if_not(len(series) >= self.K, 'The time series requires at least K={} points'.format(self.K), logger)
         self.last_k_vals = series.univariate_values()[-self.K:]
 
@@ -87,8 +86,8 @@ def __init__(self):
     def __str__(self):
         return 'Naive drift model'
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         series = self.training_series
 
     def predict(self, n: int):

darts/models/exponential_smoothing.py

@@ -65,8 +65,8 @@ def __init__(self,
     def __str__(self):
         return 'Exponential smoothing'
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         series = self.training_series
         hw_model = hw.ExponentialSmoothing(series.values(),
                                            trend=self.trend.value,

darts/models/fft.py

@@ -232,8 +232,8 @@ def __init__(self,
     def __str__(self):
         return 'FFT(nr_freqs_to_keep=' + str(self.nr_freqs_to_keep) + ', trend=' + str(self.trend) + ')'
 
-    def fit(self, series: TimeSeries, component_index: Optional[int] = None):
-        super().fit(series, component_index)
+    def fit(self, series: TimeSeries):
+        super().fit(series)
         series = self.training_series
 
         # determine trend