diff --git a/darts/models/forecasting/forecasting_model.py b/darts/models/forecasting/forecasting_model.py
index cc863d2d03..821700e3cd 100644
--- a/darts/models/forecasting/forecasting_model.py
+++ b/darts/models/forecasting/forecasting_model.py
@@ -191,8 +191,8 @@ def predict(self, n: int, num_samples: int = 1) -> TimeSeries:
         if not self._fit_called:
             raise_log(
                 ValueError(
-                    "The model must be fit before calling `predict()`."
-                    "For global models, if `predict()` is called without specifying a series,"
+                    "The model must be fit before calling predict(). "
+                    "For global models, if predict() is called without specifying a series, "
                     "the model must have been fit on a single training series."
                 ),
                 logger,
@@ -1014,8 +1014,7 @@ def predict(
             If `series` is given and is a sequence of several time series, this function returns
             a sequence where each element contains the corresponding `n` points forecasts.
         """
-        if series is None and past_covariates is None and future_covariates is None:
-            super().predict(n, num_samples)
+        super().predict(n, num_samples)
         if self._expect_past_covariates and past_covariates is None:
             raise_log(
                 ValueError(
diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py
index d1423f1b1c..8e3a23ab79 100644
--- a/darts/models/forecasting/torch_forecasting_model.py
+++ b/darts/models/forecasting/torch_forecasting_model.py
@@ -40,7 +40,10 @@
     raise_log,
     suppress_lightning_warnings,
 )
-from darts.models.forecasting.forecasting_model import GlobalForecastingModel
+from darts.models.forecasting.forecasting_model import (
+    ForecastingModel,
+    GlobalForecastingModel,
+)
 from darts.models.forecasting.pl_forecasting_module import PLForecastingModule
 from darts.timeseries import TimeSeries
 from darts.utils.data.encoders import SequentialEncoder
@@ -830,7 +833,7 @@ def fit_from_dataset(
         self
             Fitted model.
         """
-
+        self._fit_called = True
         self._verify_train_dataset_type(train_dataset)
         raise_if(
             len(train_dataset) == 0,
@@ -1173,6 +1176,10 @@ def predict_from_dataset(
         Sequence[TimeSeries]
             Returns one or more forecasts for time series.
         """
+
+        # We need to call super's super's method directly, because GlobalForecastingModel expects series:
+        ForecastingModel.predict(self, n, num_samples)
+
         self._verify_inference_dataset_type(input_series_dataset)
 
         # check that covariates and dimensions are matching what we had during training
diff --git a/darts/tests/models/forecasting/test_TFT.py b/darts/tests/models/forecasting/test_TFT.py
index ed42f5620d..3cda144c16 100644
--- a/darts/tests/models/forecasting/test_TFT.py
+++ b/darts/tests/models/forecasting/test_TFT.py
@@ -282,10 +282,14 @@ def helper_fit_predict(
                 series=series,
                 past_covariates=past_covariates,
                 future_covariates=future_covariates,
-                num_samples=100,
+                num_samples=(100 if model._is_probabilistic() else 1),
             )
+
             if isinstance(y_hat, TimeSeries):
-                y_hat = y_hat.quantile_timeseries(0.5)
+                y_hat = y_hat.quantile_timeseries(0.5) if y_hat.n_samples > 1 else y_hat
             else:
-                y_hat = [ts.quantile_timeseries(0.5) for ts in y_hat]
+                y_hat = [
+                    ts.quantile_timeseries(0.5) if ts.n_samples > 1 else ts
+                    for ts in y_hat
+                ]
             return y_hat
diff --git a/darts/tests/test_timeseries.py b/darts/tests/test_timeseries.py
index a173eb86d3..d8520a334b 100644
--- a/darts/tests/test_timeseries.py
+++ b/darts/tests/test_timeseries.py
@@ -1358,6 +1358,11 @@ def test_head_overshot_sample_axis(self):
         result = self.ts.head(20, axis="sample")
         self.assertEqual(10, result.n_samples)
 
+    def test_head_numeric_time_index(self):
+        s = TimeSeries.from_values(self.ts.values())
+        # taking the head should not crash
+        s.head()
+
     def test_tail_overshot_time_axis(self):
         result = self.ts.tail(20)
         self.assertEqual(10, result.n_timesteps)
@@ -1371,6 +1376,11 @@ def test_tail_overshot_sample_axis(self):
         result = self.ts.tail(20, axis="sample")
         self.assertEqual(10, result.n_samples)
 
+    def test_tail_numeric_time_index(self):
+        s = TimeSeries.from_values(self.ts.values())
+        # taking the tail should not crash
+        s.tail()
+
 
 class TimeSeriesFromDataFrameTestCase(DartsBaseTestClass):
     def test_from_dataframe_sunny_day(self):
diff --git a/darts/timeseries.py b/darts/timeseries.py
index 8f31a8d80f..26d581afe4 100644
--- a/darts/timeseries.py
+++ b/darts/timeseries.py
@@ -1261,8 +1261,12 @@ def head(
         """
 
         axis_str = self._get_dim_name(axis)
-        display_n = range(min(size, self._xa.sizes[axis_str]))
-        return self.__class__(self._xa[{axis_str: display_n}])
+        display_n = min(size, self._xa.sizes[axis_str])
+
+        if axis_str == self._time_dim:
+            return self[:display_n]
+        else:
+            return self.__class__(self._xa[{axis_str: range(display_n)}])
 
     def tail(
         self, size: Optional[int] = 5, axis: Optional[Union[int, str]] = 0
@@ -1284,8 +1288,12 @@ def tail(
         """
 
         axis_str = self._get_dim_name(axis)
-        display_n = range(-min(size, self._xa.sizes[axis_str]), 0)
-        return self.__class__(self._xa[{axis_str: display_n}])
+        display_n = min(size, self._xa.sizes[axis_str])
+
+        if axis_str == self._time_dim:
+            return self[-display_n:]
+        else:
+            return self.__class__(self._xa[{axis_str: range(-display_n, 0)}])
 
     def concatenate(
         self,