diff --git a/src/safeds/ml/nn/converters/_input_converter_time_series.py b/src/safeds/ml/nn/converters/_input_converter_time_series.py
index 902719c47..745c0622a 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -15,26 +15,15 @@
 
 
 class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, TimeSeriesDataset]):
-    """
-    The input conversion for a neural network, defines the input parameters for the neural network.
-
-    Parameters
-    ----------
-    window_size:
-        The size of the created windows
-    forecast_horizon:
-        The forecast horizon defines the future lag of the predicted values
-    """
+    """The input conversion for a neural network, defines the input parameters for the neural network."""
 
     def __init__(
         self,
-        window_size: int,
-        forecast_horizon: int,
         *,
         prediction_name: str = "prediction_nn",
     ) -> None:
-        self._window_size = window_size
-        self._forecast_horizon = forecast_horizon
+        self._window_size = 0
+        self._forecast_horizon = 0
         self._first = True
         self._target_name: str = ""
         self._time_name: str = ""
@@ -101,6 +90,8 @@ def _data_conversion_output(
 
     def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
         if self._first:
+            self._window_size = input_data.window_size
+            self._forecast_horizon = input_data.forecast_horizon
             self._time_name = input_data.time.name
             self._feature_names = input_data.features.column_names
             self._target_name = input_data.target.name
diff --git a/tests/safeds/ml/nn/converters/test_input_converter_time_series.py b/tests/safeds/ml/nn/converters/test_input_converter_time_series.py
index 0cc589ed9..a09a8d2e9 100644
--- a/tests/safeds/ml/nn/converters/test_input_converter_time_series.py
+++ b/tests/safeds/ml/nn/converters/test_input_converter_time_series.py
@@ -15,7 +15,7 @@
 
 def test_should_raise_if_is_fitted_is_set_correctly_lstm() -> None:
     model = NeuralNetworkRegressor(
-        InputConversionTimeSeries(1, 1, prediction_name="predicted"),
+        InputConversionTimeSeries(prediction_name="predicted"),
         [LSTMLayer(input_size=2, output_size=1)],
     )
     ts = Table.from_dict({"target": [1, 1, 1, 1], "time": [0, 0, 0, 0], "feat": [0, 0, 0, 0]}).to_time_series_dataset(
@@ -33,7 +33,7 @@ class TestEq:
     @pytest.mark.parametrize(
         ("output_conversion_ts1", "output_conversion_ts2"),
         [
-            (InputConversionTimeSeries(1, 1), InputConversionTimeSeries(1, 1)),
+            (InputConversionTimeSeries(), InputConversionTimeSeries()),
         ],
     )
     def test_should_be_equal(
@@ -47,12 +47,12 @@ def test_should_be_equal(
         ("output_conversion_ts1", "output_conversion_ts2"),
         [
             (
-                InputConversionTimeSeries(1, 1),
+                InputConversionTimeSeries(),
                 Table(),
             ),
             (
-                InputConversionTimeSeries(1, 1, prediction_name="2"),
-                InputConversionTimeSeries(1, 1, prediction_name="1"),
+                InputConversionTimeSeries( prediction_name="2"),
+                InputConversionTimeSeries( prediction_name="1"),
             ),
         ],
     )
@@ -68,7 +68,7 @@ class TestHash:
     @pytest.mark.parametrize(
         ("output_conversion_ts1", "output_conversion_ts2"),
         [
-            (InputConversionTimeSeries(1, 1), InputConversionTimeSeries(1, 1)),
+            (InputConversionTimeSeries(), InputConversionTimeSeries()),
         ],
     )
     def test_hash_should_be_equal(
@@ -79,9 +79,9 @@ def test_hash_should_be_equal(
         assert hash(output_conversion_ts1) == hash(output_conversion_ts2)
 
     def test_hash_should_not_be_equal(self) -> None:
-        output_conversion_ts1 = InputConversionTimeSeries(1, 1, prediction_name="1")
-        output_conversion_ts2 = InputConversionTimeSeries(1, 1, prediction_name="2")
-        output_conversion_ts3 = InputConversionTimeSeries(1, 1, prediction_name="3")
+        output_conversion_ts1 = InputConversionTimeSeries(prediction_name="1")
+        output_conversion_ts2 = InputConversionTimeSeries(prediction_name="2")
+        output_conversion_ts3 = InputConversionTimeSeries(prediction_name="3")
         assert hash(output_conversion_ts1) != hash(output_conversion_ts3)
         assert hash(output_conversion_ts2) != hash(output_conversion_ts1)
         assert hash(output_conversion_ts3) != hash(output_conversion_ts2)
@@ -91,9 +91,9 @@ class TestSizeOf:
     @pytest.mark.parametrize(
         "output_conversion_ts",
         [
-            InputConversionTimeSeries(1, 1, prediction_name="1"),
-            InputConversionTimeSeries(1, 1, prediction_name="2"),
-            InputConversionTimeSeries(1, 1, prediction_name="3"),
+            InputConversionTimeSeries(prediction_name="1"),
+            InputConversionTimeSeries(prediction_name="2"),
+            InputConversionTimeSeries(prediction_name="3"),
         ],
     )
     def test_should_size_be_greater_than_normal_object(
diff --git a/tests/safeds/ml/nn/test_lstm_workflow.py b/tests/safeds/ml/nn/test_lstm_workflow.py
index 5e7912b9f..85e396222 100644
--- a/tests/safeds/ml/nn/test_lstm_workflow.py
+++ b/tests/safeds/ml/nn/test_lstm_workflow.py
@@ -29,7 +29,7 @@ def test_lstm_model(device: Device) -> None:
     train_table, test_table = table.split_rows(0.8)
 
     model = NeuralNetworkRegressor(
-        InputConversionTimeSeries(window_size=7, forecast_horizon=12, prediction_name="predicted"),
+        InputConversionTimeSeries(prediction_name="predicted"),
         [ForwardLayer(input_size=7, output_size=256), LSTMLayer(input_size=256, output_size=1)],
     )
     trained_model = model.fit(