Fix/ensemble historical forecasts

darts/models/forecasting/ensemble_model.py

@@ -197,5 +197,21 @@ def ensemble(
     def min_train_series_length(self) -> int:
         return max(model.min_train_series_length for model in self.models)
 
+    @property
+    def extreme_lags(self):
+        extreme_lags_length = len(super().extreme_lags)
+
+        return [self._find_max_lag_or_none(i) for i in range(extreme_lags_length)]
+
+    def _find_max_lag_or_none(self, lag_id):
+        max_lag = None
+        for model in self.models:
+            curr_lag = model.extreme_lags[lag_id]
+            if max_lag is None or (
+                curr_lag is not None and abs(curr_lag) > abs(max_lag)
+            ):
+                max_lag = curr_lag
+        return max_lag
+
     def _is_probabilistic(self) -> bool:
         return all([model._is_probabilistic() for model in self.models])

darts/models/forecasting/theta.py

@@ -182,6 +182,10 @@ def min_train_series_length(self) -> int:
         else:
             return 3
 
+    @property
+    def extreme_lags(self):
+        return (-self.min_train_series_length, 1, None, None, None)
+
 
 class FourTheta(LocalForecastingModel):
     def __init__(
@@ -484,3 +488,7 @@ def min_train_series_length(self) -> int:
             return 2 * self.seasonality_period
         else:
             return 3
+
+    @property
+    def extreme_lags(self):
+        return (-self.min_train_series_length, 1, None, None, None)

darts/tests/models/forecasting/test_ensemble_models.py

@@ -77,6 +77,11 @@ def test_call_predict_local_models(self):
         naive_ensemble.fit(self.series1)
         naive_ensemble.predict(5)
 
+    def test_call_backtest_local_models(self):
+        naive_ensemble = NaiveEnsembleModel([NaiveSeasonal(5), Theta(2, 5)])
+        naive_ensemble.fit(self.series1)
+        naive_ensemble.backtest(self.series1)
+
     def test_predict_ensemble_local_models(self):
         naive = NaiveSeasonal(K=5)
         theta = Theta()