From 3c517477d2e454fb69c0281a53460ba1c330b64c Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Wed, 12 Jun 2024 16:19:10 +0200
Subject: [PATCH 01/15] prediction no longer takes a time series dataset only
 table

---
 docs/tutorials/time_series_forecasting.ipynb  | 84 +++++++++----------
 .../_input_converter_time_series.py           | 28 +++++--
 tests/safeds/ml/nn/test_lstm_workflow.py      | 20 +----
 3 files changed, 60 insertions(+), 72 deletions(-)

diff --git a/docs/tutorials/time_series_forecasting.ipynb b/docs/tutorials/time_series_forecasting.ipynb
index 44b53c0f6..d8588e8d0 100644
--- a/docs/tutorials/time_series_forecasting.ipynb
+++ b/docs/tutorials/time_series_forecasting.ipynb
@@ -22,14 +22,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 1,
    "outputs": [
     {
      "data": {
       "text/plain": "+------------+----------+\n| date       |    value |\n| ---        |      --- |\n| str        |      f64 |\n+=======================+\n| 1947-01-01 | 21.48000 |\n| 1947-02-01 | 21.62000 |\n| 1947-03-01 | 22.00000 |\n| 1947-04-01 | 22.00000 |\n| 1947-05-01 | 21.95000 |\n| …          |        … |\n| 1947-11-01 | 23.06000 |\n| 1947-12-01 | 23.41000 |\n| 1948-01-01 | 23.68000 |\n| 1948-02-01 | 23.67000 |\n| 1948-03-01 | 23.50000 |\n+------------+----------+",
       "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>value</th></tr><tr><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;1947-01-01&quot;</td><td>21.48</td></tr><tr><td>&quot;1947-02-01&quot;</td><td>21.62</td></tr><tr><td>&quot;1947-03-01&quot;</td><td>22.0</td></tr><tr><td>&quot;1947-04-01&quot;</td><td>22.0</td></tr><tr><td>&quot;1947-05-01&quot;</td><td>21.95</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;1947-11-01&quot;</td><td>23.06</td></tr><tr><td>&quot;1947-12-01&quot;</td><td>23.41</td></tr><tr><td>&quot;1948-01-01&quot;</td><td>23.68</td></tr><tr><td>&quot;1948-02-01&quot;</td><td>23.67</td></tr><tr><td>&quot;1948-03-01&quot;</td><td>23.5</td></tr></tbody></table></div>"
      },
-     "execution_count": 21,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,8 +47,8 @@
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:09.091832200Z",
-     "start_time": "2024-05-29T17:10:09.061123600Z"
+     "end_time": "2024-06-12T14:17:54.088140900Z",
+     "start_time": "2024-06-12T14:17:54.015617900Z"
     }
    }
   },
@@ -63,12 +63,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:09.099206700Z",
-     "start_time": "2024-05-29T17:10:09.093830500Z"
+     "end_time": "2024-06-12T14:17:54.098828300Z",
+     "start_time": "2024-06-12T14:17:54.088140900Z"
     }
    },
    "outputs": [
@@ -77,7 +77,7 @@
       "text/plain": "+------------+----------+\n| date       |    value |\n| ---        |      --- |\n| date       |      f64 |\n+=======================+\n| 1947-01-01 | 21.48000 |\n| 1947-02-01 | 21.62000 |\n| 1947-03-01 | 22.00000 |\n| 1947-04-01 | 22.00000 |\n| 1947-05-01 | 21.95000 |\n| …          |        … |\n| 1947-11-01 | 23.06000 |\n| 1947-12-01 | 23.41000 |\n| 1948-01-01 | 23.68000 |\n| 1948-02-01 | 23.67000 |\n| 1948-03-01 | 23.50000 |\n+------------+----------+",
       "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>value</th></tr><tr><td>date</td><td>f64</td></tr></thead><tbody><tr><td>1947-01-01</td><td>21.48</td></tr><tr><td>1947-02-01</td><td>21.62</td></tr><tr><td>1947-03-01</td><td>22.0</td></tr><tr><td>1947-04-01</td><td>22.0</td></tr><tr><td>1947-05-01</td><td>21.95</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1947-11-01</td><td>23.06</td></tr><tr><td>1947-12-01</td><td>23.41</td></tr><tr><td>1948-01-01</td><td>23.68</td></tr><tr><td>1948-02-01</td><td>23.67</td></tr><tr><td>1948-03-01</td><td>23.5</td></tr></tbody></table></div>"
      },
-     "execution_count": 22,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,10 +101,10 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<safeds.data.image.containers._image.Image at 0x185b4b1bc10>",
+      "text/plain": "<safeds.data.image.containers._image.Image at 0x28fa9005ed0>",
       "image/png": 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"
      },
-     "execution_count": 23,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -115,11 +115,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:09.196961400Z",
-     "start_time": "2024-05-29T17:10:09.099206700Z"
+     "end_time": "2024-06-12T14:17:57.444189600Z",
+     "start_time": "2024-06-12T14:17:54.098828300Z"
     }
    },
-   "execution_count": 23
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -132,12 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:09.197960800Z",
-     "start_time": "2024-05-29T17:10:09.174885Z"
+     "end_time": "2024-06-12T14:17:57.450287200Z",
+     "start_time": "2024-06-12T14:17:57.444189600Z"
     }
    },
    "outputs": [],
@@ -160,12 +160,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:09.198463900Z",
-     "start_time": "2024-05-29T17:10:09.181893700Z"
+     "end_time": "2024-06-12T14:17:57.462829Z",
+     "start_time": "2024-06-12T14:17:57.451285700Z"
     }
    },
    "outputs": [],
@@ -210,11 +210,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:22.072485300Z",
-     "start_time": "2024-05-29T17:10:09.184448600Z"
+     "end_time": "2024-06-12T14:18:09.269683200Z",
+     "start_time": "2024-06-12T14:17:57.463828400Z"
     }
    },
-   "execution_count": 26
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -227,26 +227,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:22.113606700Z",
-     "start_time": "2024-05-29T17:10:22.072485300Z"
+     "end_time": "2024-06-12T14:18:09.322883100Z",
+     "start_time": "2024-06-12T14:18:09.270684Z"
     }
    },
    "outputs": [],
    "source": [
     "\n",
-    "prediction = fitted_neural_network.predict(\n",
-    "    test_set.to_time_series_dataset(\n",
-    "        target_name=\"value\",\n",
-    "        window_size=10,\n",
-    "        forecast_horizon=1,\n",
-    "        continuous=False,\n",
-    "        extra_names= [\"date\"]\n",
-    "    )\n",
-    ")\n",
+    "prediction = fitted_neural_network.predict(test_set)\n",
     "prediction = prediction.to_table()"
    ]
   },
@@ -261,12 +253,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:22.114606800Z",
-     "start_time": "2024-05-29T17:10:22.112094700Z"
+     "end_time": "2024-06-12T14:18:09.326383200Z",
+     "start_time": "2024-06-12T14:18:09.323882500Z"
     }
    },
    "outputs": [],
@@ -284,29 +276,29 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:22.117044700Z",
-     "start_time": "2024-05-29T17:10:22.113606700Z"
+     "end_time": "2024-06-12T14:18:09.330134900Z",
+     "start_time": "2024-06-12T14:18:09.327385900Z"
     }
    },
-   "execution_count": 29
+   "execution_count": 9
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-05-29T17:10:22.191343300Z",
-     "start_time": "2024-05-29T17:10:22.117044700Z"
+     "end_time": "2024-06-12T14:18:09.411783500Z",
+     "start_time": "2024-06-12T14:18:09.331135700Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "<safeds.data.image.containers._image.Image at 0x185c927dc50>",
-      "image/png": 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"
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"
      },
-     "execution_count": 30,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
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 78fce0878..7a9f22277 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -5,7 +5,7 @@
 
 from safeds._utils import _structural_hash
 from safeds.data.labeled.containers import TimeSeriesDataset
-from safeds.data.tabular.containers import Column
+from safeds.data.tabular.containers import Column, Table
 
 from ._input_converter import InputConversion
 
@@ -14,7 +14,7 @@
     from torch.utils.data import DataLoader
 
 
-class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, TimeSeriesDataset]):
+class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, Table]):
     """The input conversion for a neural network, defines the input parameters for the neural network."""
 
     def __init__(
@@ -27,6 +27,7 @@ def __init__(
         self._time_name: str = ""
         self._feature_names: list[str] = []
         self._continuous: bool = False
+        self._extra_names: list[str] = []
 
     def __eq__(self, other: object) -> bool:
         if not isinstance(other, type(self)):
@@ -87,17 +88,22 @@ def _data_conversion_fit(
             continuous=self._continuous,
         )
 
-    def _data_conversion_predict(self, input_data: TimeSeriesDataset, batch_size: int) -> DataLoader:
+    def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLoader:
+        input_data = input_data.to_time_series_dataset(target_name=self._target_name,
+                                                       window_size=self._window_size,
+                                                       extra_names=self._extra_names,
+                                                       forecast_horizon=self._forecast_horizon,
+                                                       continuous=self._continuous)
         return input_data._into_dataloader_with_window_predict(self._window_size, self._forecast_horizon, batch_size)
 
     def _data_conversion_output(
         self,
-        input_data: TimeSeriesDataset,
+        input_data: Table,
         output_data: Tensor,
     ) -> TimeSeriesDataset:
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
-        input_data_table = input_data.to_table()
+        input_data_table = input_data
         input_data_table = input_data_table.slice_rows(start=window_size + forecast_horizon)
 
         return input_data_table.replace_column(
@@ -105,7 +111,7 @@ def _data_conversion_output(
             [Column(self._target_name, output_data.tolist())],
         ).to_time_series_dataset(
             target_name=self._target_name,
-            extra_names=input_data.extras.column_names,
+            extra_names=self._extra_names,
             window_size=window_size,
         )
 
@@ -117,9 +123,15 @@ def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
             self._target_name = input_data.target.name
             self._continuous = input_data._continuous
             self._first = False
+            self._extra_names = input_data.extras.column_names
         return (sorted(input_data.features.column_names)).__eq__(
             sorted(self._feature_names),
         ) and input_data.target.name == self._target_name
 
-    def _is_predict_data_valid(self, input_data: TimeSeriesDataset) -> bool:
-        return self._is_fit_data_valid(input_data)
+    def _is_predict_data_valid(self, input_data: Table) -> bool:
+        for name in self._feature_names:
+            if name not in input_data.column_names:
+                return False
+        if self._target_name not in input_data.column_names:
+            return False
+        return True
diff --git a/tests/safeds/ml/nn/test_lstm_workflow.py b/tests/safeds/ml/nn/test_lstm_workflow.py
index 2fa8f8d26..2727c5817 100644
--- a/tests/safeds/ml/nn/test_lstm_workflow.py
+++ b/tests/safeds/ml/nn/test_lstm_workflow.py
@@ -47,15 +47,7 @@ def test_lstm_model(device: Device) -> None:
         epoch_size=1,
     )
 
-    trained_model.predict(
-        test_table.to_time_series_dataset(
-            "value",
-            window_size=7,
-            forecast_horizon=12,
-            continuous=True,
-            extra_names=["date"],
-        ),
-    )
+    trained_model.predict(test_table)
     trained_model_2 = model_2.fit(
         train_table.to_time_series_dataset(
             "value",
@@ -67,14 +59,6 @@ def test_lstm_model(device: Device) -> None:
         epoch_size=1,
     )
 
-    trained_model_2.predict(
-        test_table.to_time_series_dataset(
-            "value",
-            window_size=7,
-            forecast_horizon=12,
-            continuous=False,
-            extra_names=["date"],
-        ),
-    )
+    trained_model_2.predict(test_table)
     assert trained_model._model is not None
     assert trained_model._model.state_dict()["_pytorch_layers.0._layer.weight"].device == _get_device()

From 35e3a7a84a0ab2c3bae250340c32406f6353cf81 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Wed, 12 Jun 2024 16:34:41 +0200
Subject: [PATCH 02/15] added type notations for mypy

---
 docs/tutorials/time_series_forecasting.ipynb  | 77 +++++++++----------
 .../_input_converter_time_series.py           | 10 +--
 .../test_input_converter_time_series.py       |  2 +-
 3 files changed, 42 insertions(+), 47 deletions(-)

diff --git a/docs/tutorials/time_series_forecasting.ipynb b/docs/tutorials/time_series_forecasting.ipynb
index d8588e8d0..487e549d2 100644
--- a/docs/tutorials/time_series_forecasting.ipynb
+++ b/docs/tutorials/time_series_forecasting.ipynb
@@ -22,14 +22,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "outputs": [
     {
      "data": {
       "text/plain": "+------------+----------+\n| date       |    value |\n| ---        |      --- |\n| str        |      f64 |\n+=======================+\n| 1947-01-01 | 21.48000 |\n| 1947-02-01 | 21.62000 |\n| 1947-03-01 | 22.00000 |\n| 1947-04-01 | 22.00000 |\n| 1947-05-01 | 21.95000 |\n| …          |        … |\n| 1947-11-01 | 23.06000 |\n| 1947-12-01 | 23.41000 |\n| 1948-01-01 | 23.68000 |\n| 1948-02-01 | 23.67000 |\n| 1948-03-01 | 23.50000 |\n+------------+----------+",
       "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>value</th></tr><tr><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;1947-01-01&quot;</td><td>21.48</td></tr><tr><td>&quot;1947-02-01&quot;</td><td>21.62</td></tr><tr><td>&quot;1947-03-01&quot;</td><td>22.0</td></tr><tr><td>&quot;1947-04-01&quot;</td><td>22.0</td></tr><tr><td>&quot;1947-05-01&quot;</td><td>21.95</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;1947-11-01&quot;</td><td>23.06</td></tr><tr><td>&quot;1947-12-01&quot;</td><td>23.41</td></tr><tr><td>&quot;1948-01-01&quot;</td><td>23.68</td></tr><tr><td>&quot;1948-02-01&quot;</td><td>23.67</td></tr><tr><td>&quot;1948-03-01&quot;</td><td>23.5</td></tr></tbody></table></div>"
      },
-     "execution_count": 1,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,8 +47,8 @@
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:17:54.088140900Z",
-     "start_time": "2024-06-12T14:17:54.015617900Z"
+     "end_time": "2024-06-12T14:20:53.930750700Z",
+     "start_time": "2024-06-12T14:20:53.911358300Z"
     }
    }
   },
@@ -63,12 +63,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:17:54.098828300Z",
-     "start_time": "2024-06-12T14:17:54.088140900Z"
+     "end_time": "2024-06-12T14:20:53.938835900Z",
+     "start_time": "2024-06-12T14:20:53.933761600Z"
     }
    },
    "outputs": [
@@ -77,7 +77,7 @@
       "text/plain": "+------------+----------+\n| date       |    value |\n| ---        |      --- |\n| date       |      f64 |\n+=======================+\n| 1947-01-01 | 21.48000 |\n| 1947-02-01 | 21.62000 |\n| 1947-03-01 | 22.00000 |\n| 1947-04-01 | 22.00000 |\n| 1947-05-01 | 21.95000 |\n| …          |        … |\n| 1947-11-01 | 23.06000 |\n| 1947-12-01 | 23.41000 |\n| 1948-01-01 | 23.68000 |\n| 1948-02-01 | 23.67000 |\n| 1948-03-01 | 23.50000 |\n+------------+----------+",
       "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>value</th></tr><tr><td>date</td><td>f64</td></tr></thead><tbody><tr><td>1947-01-01</td><td>21.48</td></tr><tr><td>1947-02-01</td><td>21.62</td></tr><tr><td>1947-03-01</td><td>22.0</td></tr><tr><td>1947-04-01</td><td>22.0</td></tr><tr><td>1947-05-01</td><td>21.95</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1947-11-01</td><td>23.06</td></tr><tr><td>1947-12-01</td><td>23.41</td></tr><tr><td>1948-01-01</td><td>23.68</td></tr><tr><td>1948-02-01</td><td>23.67</td></tr><tr><td>1948-03-01</td><td>23.5</td></tr></tbody></table></div>"
      },
-     "execution_count": 2,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,10 +101,10 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<safeds.data.image.containers._image.Image at 0x28fa9005ed0>",
+      "text/plain": "<safeds.data.image.containers._image.Image at 0x22039e00b10>",
       "image/png": 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"
      },
-     "execution_count": 3,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -115,11 +115,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:17:57.444189600Z",
-     "start_time": "2024-06-12T14:17:54.098828300Z"
+     "end_time": "2024-06-12T14:20:54.060832500Z",
+     "start_time": "2024-06-12T14:20:53.938835900Z"
     }
    },
-   "execution_count": 3
+   "execution_count": 13
   },
   {
    "cell_type": "markdown",
@@ -132,12 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:17:57.450287200Z",
-     "start_time": "2024-06-12T14:17:57.444189600Z"
+     "end_time": "2024-06-12T14:20:54.082461400Z",
+     "start_time": "2024-06-12T14:20:54.042536Z"
     }
    },
    "outputs": [],
@@ -160,12 +160,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:17:57.462829Z",
-     "start_time": "2024-06-12T14:17:57.451285700Z"
+     "end_time": "2024-06-12T14:20:54.083463200Z",
+     "start_time": "2024-06-12T14:20:54.048298Z"
     }
    },
    "outputs": [],
@@ -210,11 +210,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:18:09.269683200Z",
-     "start_time": "2024-06-12T14:17:57.463828400Z"
+     "end_time": "2024-06-12T14:21:07.575824700Z",
+     "start_time": "2024-06-12T14:20:54.051321200Z"
     }
    },
-   "execution_count": 6
+   "execution_count": 16
   },
   {
    "cell_type": "markdown",
@@ -227,19 +227,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:18:09.322883100Z",
-     "start_time": "2024-06-12T14:18:09.270684Z"
+     "end_time": "2024-06-12T14:21:07.625640300Z",
+     "start_time": "2024-06-12T14:21:07.576824400Z"
     }
    },
    "outputs": [],
    "source": [
     "\n",
-    "prediction = fitted_neural_network.predict(test_set)\n",
-    "prediction = prediction.to_table()"
+    "prediction = fitted_neural_network.predict(test_set)"
    ]
   },
   {
@@ -253,12 +252,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:18:09.326383200Z",
-     "start_time": "2024-06-12T14:18:09.323882500Z"
+     "end_time": "2024-06-12T14:21:07.629330100Z",
+     "start_time": "2024-06-12T14:21:07.626640900Z"
     }
    },
    "outputs": [],
@@ -276,29 +275,29 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:18:09.330134900Z",
-     "start_time": "2024-06-12T14:18:09.327385900Z"
+     "end_time": "2024-06-12T14:21:07.633764600Z",
+     "start_time": "2024-06-12T14:21:07.630835200Z"
     }
    },
-   "execution_count": 9
+   "execution_count": 19
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:18:09.411783500Z",
-     "start_time": "2024-06-12T14:18:09.331135700Z"
+     "end_time": "2024-06-12T14:21:07.733180200Z",
+     "start_time": "2024-06-12T14:21:07.634764300Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "<safeds.data.image.containers._image.Image at 0x28ffb2db550>",
-      "image/png": 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"
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"
      },
-     "execution_count": 10,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
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 7a9f22277..03e951fe2 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -89,18 +89,18 @@ def _data_conversion_fit(
         )
 
     def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLoader:
-        input_data = input_data.to_time_series_dataset(target_name=self._target_name,
+        input_data_time_series: TimeSeriesDataset = input_data.to_time_series_dataset(target_name=self._target_name,
                                                        window_size=self._window_size,
                                                        extra_names=self._extra_names,
                                                        forecast_horizon=self._forecast_horizon,
                                                        continuous=self._continuous)
-        return input_data._into_dataloader_with_window_predict(self._window_size, self._forecast_horizon, batch_size)
+        return input_data_time_series._into_dataloader_with_window_predict(self._window_size, self._forecast_horizon, batch_size)
 
     def _data_conversion_output(
         self,
         input_data: Table,
         output_data: Tensor,
-    ) -> TimeSeriesDataset:
+    ) -> Table:
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
         input_data_table = input_data
@@ -109,10 +109,6 @@ def _data_conversion_output(
         return input_data_table.replace_column(
             self._target_name,
             [Column(self._target_name, output_data.tolist())],
-        ).to_time_series_dataset(
-            target_name=self._target_name,
-            extra_names=self._extra_names,
-            window_size=window_size,
         )
 
     def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
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 606c8a520..c5e7d86b6 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
@@ -24,7 +24,7 @@ def test_should_raise_if_is_fitted_is_set_correctly_lstm() -> None:
     )
     assert not model.is_fitted
     model = model.fit(ts)
-    model.predict(ts)
+    model.predict(ts.to_table())
     assert model.is_fitted
 
 

From 456c6511ad890eab029d2f719612ac777d4028fd Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Wed, 12 Jun 2024 16:57:00 +0200
Subject: [PATCH 03/15] returns a TimeSeriesDataset again

---
 docs/tutorials/time_series_forecasting.ipynb  | 79 ++++++++++---------
 .../_input_converter_time_series.py           | 10 ++-
 2 files changed, 47 insertions(+), 42 deletions(-)

diff --git a/docs/tutorials/time_series_forecasting.ipynb b/docs/tutorials/time_series_forecasting.ipynb
index 487e549d2..7ba699a30 100644
--- a/docs/tutorials/time_series_forecasting.ipynb
+++ b/docs/tutorials/time_series_forecasting.ipynb
@@ -22,14 +22,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "outputs": [
     {
      "data": {
       "text/plain": "+------------+----------+\n| date       |    value |\n| ---        |      --- |\n| str        |      f64 |\n+=======================+\n| 1947-01-01 | 21.48000 |\n| 1947-02-01 | 21.62000 |\n| 1947-03-01 | 22.00000 |\n| 1947-04-01 | 22.00000 |\n| 1947-05-01 | 21.95000 |\n| …          |        … |\n| 1947-11-01 | 23.06000 |\n| 1947-12-01 | 23.41000 |\n| 1948-01-01 | 23.68000 |\n| 1948-02-01 | 23.67000 |\n| 1948-03-01 | 23.50000 |\n+------------+----------+",
       "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>value</th></tr><tr><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;1947-01-01&quot;</td><td>21.48</td></tr><tr><td>&quot;1947-02-01&quot;</td><td>21.62</td></tr><tr><td>&quot;1947-03-01&quot;</td><td>22.0</td></tr><tr><td>&quot;1947-04-01&quot;</td><td>22.0</td></tr><tr><td>&quot;1947-05-01&quot;</td><td>21.95</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;1947-11-01&quot;</td><td>23.06</td></tr><tr><td>&quot;1947-12-01&quot;</td><td>23.41</td></tr><tr><td>&quot;1948-01-01&quot;</td><td>23.68</td></tr><tr><td>&quot;1948-02-01&quot;</td><td>23.67</td></tr><tr><td>&quot;1948-03-01&quot;</td><td>23.5</td></tr></tbody></table></div>"
      },
-     "execution_count": 11,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,8 +47,8 @@
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:20:53.930750700Z",
-     "start_time": "2024-06-12T14:20:53.911358300Z"
+     "end_time": "2024-06-12T14:56:18.647123700Z",
+     "start_time": "2024-06-12T14:56:18.624006Z"
     }
    }
   },
@@ -63,12 +63,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:20:53.938835900Z",
-     "start_time": "2024-06-12T14:20:53.933761600Z"
+     "end_time": "2024-06-12T14:56:18.656269200Z",
+     "start_time": "2024-06-12T14:56:18.649123200Z"
     }
    },
    "outputs": [
@@ -77,7 +77,7 @@
       "text/plain": "+------------+----------+\n| date       |    value |\n| ---        |      --- |\n| date       |      f64 |\n+=======================+\n| 1947-01-01 | 21.48000 |\n| 1947-02-01 | 21.62000 |\n| 1947-03-01 | 22.00000 |\n| 1947-04-01 | 22.00000 |\n| 1947-05-01 | 21.95000 |\n| …          |        … |\n| 1947-11-01 | 23.06000 |\n| 1947-12-01 | 23.41000 |\n| 1948-01-01 | 23.68000 |\n| 1948-02-01 | 23.67000 |\n| 1948-03-01 | 23.50000 |\n+------------+----------+",
       "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>value</th></tr><tr><td>date</td><td>f64</td></tr></thead><tbody><tr><td>1947-01-01</td><td>21.48</td></tr><tr><td>1947-02-01</td><td>21.62</td></tr><tr><td>1947-03-01</td><td>22.0</td></tr><tr><td>1947-04-01</td><td>22.0</td></tr><tr><td>1947-05-01</td><td>21.95</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1947-11-01</td><td>23.06</td></tr><tr><td>1947-12-01</td><td>23.41</td></tr><tr><td>1948-01-01</td><td>23.68</td></tr><tr><td>1948-02-01</td><td>23.67</td></tr><tr><td>1948-03-01</td><td>23.5</td></tr></tbody></table></div>"
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,10 +101,10 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<safeds.data.image.containers._image.Image at 0x22039e00b10>",
+      "text/plain": "<safeds.data.image.containers._image.Image at 0x1ad22823950>",
       "image/png": 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"
      },
-     "execution_count": 13,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -115,11 +115,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:20:54.060832500Z",
-     "start_time": "2024-06-12T14:20:53.938835900Z"
+     "end_time": "2024-06-12T14:56:18.778402800Z",
+     "start_time": "2024-06-12T14:56:18.653758900Z"
     }
    },
-   "execution_count": 13
+   "execution_count": 23
   },
   {
    "cell_type": "markdown",
@@ -132,12 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:20:54.082461400Z",
-     "start_time": "2024-06-12T14:20:54.042536Z"
+     "end_time": "2024-06-12T14:56:18.781401Z",
+     "start_time": "2024-06-12T14:56:18.731420200Z"
     }
    },
    "outputs": [],
@@ -160,12 +160,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:20:54.083463200Z",
-     "start_time": "2024-06-12T14:20:54.048298Z"
+     "end_time": "2024-06-12T14:56:18.781401Z",
+     "start_time": "2024-06-12T14:56:18.737179600Z"
     }
    },
    "outputs": [],
@@ -182,7 +182,7 @@
     "\n",
     "neural_network = NeuralNetworkRegressor(\n",
     "    InputConversionTimeSeries(),\n",
-    "    [ForwardLayer(256), LSTMLayer(128), ForwardLayer(1)],\n",
+    "    [ForwardLayer(256), LSTMLayer(512), ForwardLayer(1)],\n",
     ")"
    ]
   },
@@ -210,11 +210,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:21:07.575824700Z",
-     "start_time": "2024-06-12T14:20:54.051321200Z"
+     "end_time": "2024-06-12T14:56:30.949080300Z",
+     "start_time": "2024-06-12T14:56:18.739691600Z"
     }
    },
-   "execution_count": 16
+   "execution_count": 26
   },
   {
    "cell_type": "markdown",
@@ -227,18 +227,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:21:07.625640300Z",
-     "start_time": "2024-06-12T14:21:07.576824400Z"
+     "end_time": "2024-06-12T14:56:30.989575200Z",
+     "start_time": "2024-06-12T14:56:30.950080700Z"
     }
    },
    "outputs": [],
    "source": [
     "\n",
-    "prediction = fitted_neural_network.predict(test_set)"
+    "prediction = fitted_neural_network.predict(test_set)\n",
+    "prediction = prediction.to_table()"
    ]
   },
   {
@@ -252,12 +253,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:21:07.629330100Z",
-     "start_time": "2024-06-12T14:21:07.626640900Z"
+     "end_time": "2024-06-12T14:56:30.991965600Z",
+     "start_time": "2024-06-12T14:56:30.989575200Z"
     }
    },
    "outputs": [],
@@ -275,29 +276,29 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:21:07.633764600Z",
-     "start_time": "2024-06-12T14:21:07.630835200Z"
+     "end_time": "2024-06-12T14:56:30.997483900Z",
+     "start_time": "2024-06-12T14:56:30.993971900Z"
     }
    },
-   "execution_count": 19
+   "execution_count": 29
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-12T14:21:07.733180200Z",
-     "start_time": "2024-06-12T14:21:07.634764300Z"
+     "end_time": "2024-06-12T14:56:31.078460800Z",
+     "start_time": "2024-06-12T14:56:30.996484900Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "<safeds.data.image.containers._image.Image at 0x22039dcb950>",
-      "image/png": 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"
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"
      },
-     "execution_count": 20,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
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 03e951fe2..3a5bd3d8b 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -89,7 +89,7 @@ def _data_conversion_fit(
         )
 
     def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLoader:
-        input_data_time_series: TimeSeriesDataset = input_data.to_time_series_dataset(target_name=self._target_name,
+        input_data_time_series = input_data.to_time_series_dataset(target_name=self._target_name,
                                                        window_size=self._window_size,
                                                        extra_names=self._extra_names,
                                                        forecast_horizon=self._forecast_horizon,
@@ -100,7 +100,7 @@ def _data_conversion_output(
         self,
         input_data: Table,
         output_data: Tensor,
-    ) -> Table:
+    ) -> TimeSeriesDataset:
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
         input_data_table = input_data
@@ -109,7 +109,11 @@ def _data_conversion_output(
         return input_data_table.replace_column(
             self._target_name,
             [Column(self._target_name, output_data.tolist())],
-        )
+        ).to_time_series_dataset(target_name=self._target_name,
+                                 window_size=self._window_size,
+                                 extra_names=self._extra_names,
+                                 forecast_horizon=self._forecast_horizon,
+                                 continuous=self._continuous)
 
     def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
         if self._first:

From 8fc059e51eee084c85cf4a0cf554e6bbe95cf202 Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Wed, 12 Jun 2024 14:58:29 +0000
Subject: [PATCH 04/15] style: apply automated linter fixes

---
 .../_input_converter_time_series.py           | 28 +++++++++++--------
 1 file changed, 17 insertions(+), 11 deletions(-)

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 3a5bd3d8b..04e85428b 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -89,12 +89,16 @@ def _data_conversion_fit(
         )
 
     def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLoader:
-        input_data_time_series = input_data.to_time_series_dataset(target_name=self._target_name,
-                                                       window_size=self._window_size,
-                                                       extra_names=self._extra_names,
-                                                       forecast_horizon=self._forecast_horizon,
-                                                       continuous=self._continuous)
-        return input_data_time_series._into_dataloader_with_window_predict(self._window_size, self._forecast_horizon, batch_size)
+        input_data_time_series = input_data.to_time_series_dataset(
+            target_name=self._target_name,
+            window_size=self._window_size,
+            extra_names=self._extra_names,
+            forecast_horizon=self._forecast_horizon,
+            continuous=self._continuous,
+        )
+        return input_data_time_series._into_dataloader_with_window_predict(
+            self._window_size, self._forecast_horizon, batch_size,
+        )
 
     def _data_conversion_output(
         self,
@@ -109,11 +113,13 @@ def _data_conversion_output(
         return input_data_table.replace_column(
             self._target_name,
             [Column(self._target_name, output_data.tolist())],
-        ).to_time_series_dataset(target_name=self._target_name,
-                                 window_size=self._window_size,
-                                 extra_names=self._extra_names,
-                                 forecast_horizon=self._forecast_horizon,
-                                 continuous=self._continuous)
+        ).to_time_series_dataset(
+            target_name=self._target_name,
+            window_size=self._window_size,
+            extra_names=self._extra_names,
+            forecast_horizon=self._forecast_horizon,
+            continuous=self._continuous,
+        )
 
     def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
         if self._first:

From 33ddba9873c50a711660e6377db1ce759142a505 Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Wed, 12 Jun 2024 15:00:00 +0000
Subject: [PATCH 05/15] style: apply automated linter fixes

---
 src/safeds/ml/nn/converters/_input_converter_time_series.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

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 04e85428b..8b81cce5b 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -97,7 +97,9 @@ def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLo
             continuous=self._continuous,
         )
         return input_data_time_series._into_dataloader_with_window_predict(
-            self._window_size, self._forecast_horizon, batch_size,
+            self._window_size,
+            self._forecast_horizon,
+            batch_size,
         )
 
     def _data_conversion_output(

From edd9883b0c0031393317f1dfd513c50c39415bc8 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Wed, 12 Jun 2024 17:05:18 +0200
Subject: [PATCH 06/15] added code cov for is_predict_data_valid

---
 .../ml/nn/converters/test_input_converter_time_series.py  | 8 ++++++++
 1 file changed, 8 insertions(+)

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 c5e7d86b6..58830a05c 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
@@ -28,6 +28,14 @@ def test_should_raise_if_is_fitted_is_set_correctly_lstm() -> None:
     assert model.is_fitted
 
 
+def test_is_predict_data_valid() -> None:
+    input_conv = InputConversionTimeSeries()
+    data = Table({"target": [1, 1, 1, 1], "time": [0, 0, 0, 0], "feat": [0, 0, 0, 0]})
+    assert not input_conv._is_predict_data_valid(data)
+    input_conv._feature_names = ["XYZ"]
+    assert not input_conv._is_predict_data_valid(data)
+
+
 class TestEq:
     @pytest.mark.parametrize(
         ("output_conversion_ts1", "output_conversion_ts2"),

From 716f9176d2314f74fb58a0b50c12924d5c8235a9 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 12:57:31 +0200
Subject: [PATCH 07/15] fixed init predict takes now table and timeseries
 dataset for timeseries conversion

---
 docs/tutorials/time_series_forecasting.ipynb  |  2 +-
 .../data/tabular/containers/__init__.py       |  2 +-
 .../_input_converter_time_series.py           | 51 +++++++++++--------
 tests/safeds/ml/nn/test_lstm_workflow.py      |  8 ++-
 4 files changed, 39 insertions(+), 24 deletions(-)

diff --git a/docs/tutorials/time_series_forecasting.ipynb b/docs/tutorials/time_series_forecasting.ipynb
index 7ba699a30..e59d89224 100644
--- a/docs/tutorials/time_series_forecasting.ipynb
+++ b/docs/tutorials/time_series_forecasting.ipynb
@@ -264,7 +264,7 @@
    "outputs": [],
    "source": [
     "prediction = trained_scaler.inverse_transform(prediction)\n",
-    "test_set = trained_scaler.inverse_transform(test_set)\n"
+    "test_set = trained_scaler.inverse_transform(test_set)"
    ]
   },
   {
diff --git a/src/safeds/data/tabular/containers/__init__.py b/src/safeds/data/tabular/containers/__init__.py
index f1ffc3110..18cc631cd 100644
--- a/src/safeds/data/tabular/containers/__init__.py
+++ b/src/safeds/data/tabular/containers/__init__.py
@@ -19,7 +19,7 @@
         "Column": "._column:Column",
         "Row": "._row:Row",
         "StringCell": "._string_cell:StringCell",
-        "TemporalCell": "._temporal_cell:",
+        "TemporalCell": "._temporal_cell:TemporalCell",
         "Table": "._table:Table",
     },
 )
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 8b81cce5b..5d8a5861a 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -24,7 +24,6 @@ def __init__(
         self._forecast_horizon = 0
         self._first = True
         self._target_name: str = ""
-        self._time_name: str = ""
         self._feature_names: list[str] = []
         self._continuous: bool = False
         self._extra_names: list[str] = []
@@ -47,7 +46,6 @@ def __hash__(self) -> int:
             self._window_size,
             self._forecast_horizon,
             self._target_name,
-            self._time_name,
             self._feature_names,
             self._continuous,
         )
@@ -57,7 +55,6 @@ def __sizeof__(self) -> int:
             sys.getsizeof(self._window_size)
             + sys.getsizeof(self._forecast_horizon)
             + sys.getsizeof(self._target_name)
-            + sys.getsizeof(self._time_name)
             + sys.getsizeof(self._feature_names)
             + sys.getsizeof(self._continuous)
         )
@@ -88,15 +85,17 @@ def _data_conversion_fit(
             continuous=self._continuous,
         )
 
-    def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLoader:
-        input_data_time_series = input_data.to_time_series_dataset(
-            target_name=self._target_name,
-            window_size=self._window_size,
-            extra_names=self._extra_names,
-            forecast_horizon=self._forecast_horizon,
-            continuous=self._continuous,
-        )
-        return input_data_time_series._into_dataloader_with_window_predict(
+    def _data_conversion_predict(self, input_data: Table | TimeSeriesDataset, batch_size: int) -> DataLoader:
+        if type(input_data) == Table:
+            input_data = input_data.to_time_series_dataset(
+                target_name=self._target_name,
+                window_size=self._window_size,
+                extra_names=self._extra_names,
+                forecast_horizon=self._forecast_horizon,
+                continuous=self._continuous,
+            )
+
+        return input_data._into_dataloader_with_window_predict(
             self._window_size,
             self._forecast_horizon,
             batch_size,
@@ -104,13 +103,15 @@ def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLo
 
     def _data_conversion_output(
         self,
-        input_data: Table,
+        input_data: Table | TimeSeriesDataset,
         output_data: Tensor,
     ) -> TimeSeriesDataset:
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
-        input_data_table = input_data
-        input_data_table = input_data_table.slice_rows(start=window_size + forecast_horizon)
+        if type(input_data) == TimeSeriesDataset:
+            input_data = input_data.to_table()
+
+        input_data_table = input_data.slice_rows(start=window_size + forecast_horizon)
 
         return input_data_table.replace_column(
             self._target_name,
@@ -136,10 +137,18 @@ def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
             sorted(self._feature_names),
         ) and input_data.target.name == self._target_name
 
-    def _is_predict_data_valid(self, input_data: Table) -> bool:
-        for name in self._feature_names:
-            if name not in input_data.column_names:
+    def _is_predict_data_valid(self, input_data: Table | TimeSeriesDataset) -> bool:
+        if type(input_data) == Table:
+            for name in self._feature_names:
+                if name not in input_data.column_names:
+                    return False
+            if self._target_name not in input_data.column_names:
                 return False
-        if self._target_name not in input_data.column_names:
-            return False
-        return True
+            return True
+        if type(input_data) == TimeSeriesDataset:
+            return (self._target_name == input_data.target.name
+                    and self._feature_names == input_data.features.column_names
+                    and self._extra_names == input_data.extras.column_names
+                    and self._forecast_horizon == input_data.forecast_horizon
+                    and self._window_size == input_data.window_size
+                    and self._continuous == input_data.continuous)
diff --git a/tests/safeds/ml/nn/test_lstm_workflow.py b/tests/safeds/ml/nn/test_lstm_workflow.py
index 2727c5817..64473995c 100644
--- a/tests/safeds/ml/nn/test_lstm_workflow.py
+++ b/tests/safeds/ml/nn/test_lstm_workflow.py
@@ -59,6 +59,12 @@ def test_lstm_model(device: Device) -> None:
         epoch_size=1,
     )
 
-    trained_model_2.predict(test_table)
+    trained_model_2.predict(test_table.to_time_series_dataset(
+            "value",
+            window_size=7,
+            forecast_horizon=12,
+            continuous=False,
+            extra_names=["date"],
+        ))
     assert trained_model._model is not None
     assert trained_model._model.state_dict()["_pytorch_layers.0._layer.weight"].device == _get_device()

From b35ec7f080172e33fd155f73d4933491bb5bdd01 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 14:10:58 +0200
Subject: [PATCH 08/15] for some reason the numerical grouped snapshot fails

---
 src/safeds/ml/nn/_model.py                    |  2 +-
 .../_input_converter_time_series.py           | 25 +++++++++++--------
 2 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py
index 3d9086d61..b88cd4a3a 100644
--- a/src/safeds/ml/nn/_model.py
+++ b/src/safeds/ml/nn/_model.py
@@ -258,7 +258,7 @@ def fit(
         copied_model._model.eval()
         return copied_model
 
-    def predict(self, test_data: IPT) -> IFT:
+    def predict(self, test_data: IPT | IFT) -> IFT:
         """
         Make a prediction for the given test data.
 
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 5d8a5861a..be05f5490 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -86,16 +86,18 @@ def _data_conversion_fit(
         )
 
     def _data_conversion_predict(self, input_data: Table | TimeSeriesDataset, batch_size: int) -> DataLoader:
-        if type(input_data) == Table:
-            input_data = input_data.to_time_series_dataset(
+        if isinstance(input_data, Table):
+            data: TimeSeriesDataset = input_data.to_time_series_dataset(
                 target_name=self._target_name,
                 window_size=self._window_size,
                 extra_names=self._extra_names,
                 forecast_horizon=self._forecast_horizon,
                 continuous=self._continuous,
             )
+        else:
+            data: TimeSeriesDataset = input_data
 
-        return input_data._into_dataloader_with_window_predict(
+        return data._into_dataloader_with_window_predict(
             self._window_size,
             self._forecast_horizon,
             batch_size,
@@ -108,10 +110,12 @@ def _data_conversion_output(
     ) -> TimeSeriesDataset:
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
-        if type(input_data) == TimeSeriesDataset:
-            input_data = input_data.to_table()
+        if isinstance(input_data, TimeSeriesDataset):
+            table_data: Table = input_data.to_table()
+        else:
+            table_data: Table = input_data
 
-        input_data_table = input_data.slice_rows(start=window_size + forecast_horizon)
+        input_data_table = table_data.slice_rows(start=window_size + forecast_horizon)
 
         return input_data_table.replace_column(
             self._target_name,
@@ -133,19 +137,18 @@ def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
             self._continuous = input_data._continuous
             self._first = False
             self._extra_names = input_data.extras.column_names
-        return (sorted(input_data.features.column_names)).__eq__(
-            sorted(self._feature_names),
-        ) and input_data.target.name == self._target_name
+        return (sorted(input_data.features.column_names).__eq__(
+            sorted(self._feature_names), ) and input_data.target.name == self._target_name)
 
     def _is_predict_data_valid(self, input_data: Table | TimeSeriesDataset) -> bool:
-        if type(input_data) == Table:
+        if isinstance(input_data, Table):
             for name in self._feature_names:
                 if name not in input_data.column_names:
                     return False
             if self._target_name not in input_data.column_names:
                 return False
             return True
-        if type(input_data) == TimeSeriesDataset:
+        if isinstance(input_data, TimeSeriesDataset):
             return (self._target_name == input_data.target.name
                     and self._feature_names == input_data.features.column_names
                     and self._extra_names == input_data.extras.column_names

From 791956f8a9d1af0446a37ee8a0f58e6f962d7c4f Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 14:15:13 +0200
Subject: [PATCH 09/15] fixed linter

---
 src/safeds/ml/nn/_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py
index b88cd4a3a..3d9086d61 100644
--- a/src/safeds/ml/nn/_model.py
+++ b/src/safeds/ml/nn/_model.py
@@ -258,7 +258,7 @@ def fit(
         copied_model._model.eval()
         return copied_model
 
-    def predict(self, test_data: IPT | IFT) -> IFT:
+    def predict(self, test_data: IPT) -> IFT:
         """
         Make a prediction for the given test data.
 

From d1f12f6e3612050d0f285664cfcb9792866ab2ab Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 14:29:18 +0200
Subject: [PATCH 10/15] fixed linter

---
 .../ml/nn/converters/_input_converter_time_series.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

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 be05f5490..e9b0843ad 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -14,7 +14,7 @@
     from torch.utils.data import DataLoader
 
 
-class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, Table]):
+class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, Table | TimeSeriesDataset]):
     """The input conversion for a neural network, defines the input parameters for the neural network."""
 
     def __init__(
@@ -86,8 +86,9 @@ def _data_conversion_fit(
         )
 
     def _data_conversion_predict(self, input_data: Table | TimeSeriesDataset, batch_size: int) -> DataLoader:
+        data: TimeSeriesDataset
         if isinstance(input_data, Table):
-            data: TimeSeriesDataset = input_data.to_time_series_dataset(
+            data = input_data.to_time_series_dataset(
                 target_name=self._target_name,
                 window_size=self._window_size,
                 extra_names=self._extra_names,
@@ -95,7 +96,7 @@ def _data_conversion_predict(self, input_data: Table | TimeSeriesDataset, batch_
                 continuous=self._continuous,
             )
         else:
-            data: TimeSeriesDataset = input_data
+            data = input_data
 
         return data._into_dataloader_with_window_predict(
             self._window_size,
@@ -108,12 +109,13 @@ def _data_conversion_output(
         input_data: Table | TimeSeriesDataset,
         output_data: Tensor,
     ) -> TimeSeriesDataset:
+        table_data: Table
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
         if isinstance(input_data, TimeSeriesDataset):
-            table_data: Table = input_data.to_table()
+            table_data = input_data.to_table()
         else:
-            table_data: Table = input_data
+            table_data = input_data
 
         input_data_table = table_data.slice_rows(start=window_size + forecast_horizon)
 

From 33d381267c77727a05e84d17aa0a7551713a130c Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 14:43:50 +0200
Subject: [PATCH 11/15] fixed linter

---
 src/safeds/ml/nn/_model.py                                  | 6 +++---
 src/safeds/ml/nn/converters/_input_converter_time_series.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py
index 3d9086d61..a36bf0195 100644
--- a/src/safeds/ml/nn/_model.py
+++ b/src/safeds/ml/nn/_model.py
@@ -6,7 +6,7 @@
 from safeds._config import _init_default_device
 from safeds._validation import _check_bounds, _ClosedBound
 from safeds.data.image.containers import ImageList
-from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset
+from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset, Dataset
 from safeds.data.labeled.containers._image_dataset import _ColumnAsTensor
 from safeds.data.tabular.containers import Table
 from safeds.data.tabular.transformation import OneHotEncoder
@@ -258,7 +258,7 @@ def fit(
         copied_model._model.eval()
         return copied_model
 
-    def predict(self, test_data: IPT) -> IFT:
+    def predict(self, test_data: IPT | Dataset) -> IFT:
         """
         Make a prediction for the given test data.
 
@@ -562,7 +562,7 @@ def fit(
         copied_model._model.eval()
         return copied_model
 
-    def predict(self, test_data: IPT) -> IFT:
+    def predict(self, test_data: IPT | Dataset) -> IFT:
         """
         Make a prediction for the given test data.
 
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 e9b0843ad..71c9cac33 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -14,7 +14,7 @@
     from torch.utils.data import DataLoader
 
 
-class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, Table | TimeSeriesDataset]):
+class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, Table]):
     """The input conversion for a neural network, defines the input parameters for the neural network."""
 
     def __init__(

From 73f76812efaab8c41913ef77685c09dd2d695347 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 19:01:22 +0200
Subject: [PATCH 12/15] removed that it takes also time series dataset

---
 .../_input_converter_time_series.py           | 48 +++++++------------
 tests/safeds/ml/nn/test_lstm_workflow.py      |  8 +---
 2 files changed, 17 insertions(+), 39 deletions(-)

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 71c9cac33..52ce3023e 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -85,19 +85,14 @@ def _data_conversion_fit(
             continuous=self._continuous,
         )
 
-    def _data_conversion_predict(self, input_data: Table | TimeSeriesDataset, batch_size: int) -> DataLoader:
+    def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLoader:
         data: TimeSeriesDataset
-        if isinstance(input_data, Table):
-            data = input_data.to_time_series_dataset(
-                target_name=self._target_name,
-                window_size=self._window_size,
-                extra_names=self._extra_names,
-                forecast_horizon=self._forecast_horizon,
-                continuous=self._continuous,
-            )
-        else:
-            data = input_data
-
+        data = input_data.to_time_series_dataset(
+            target_name=self._target_name,
+            window_size=self._window_size,
+            extra_names=self._extra_names,
+            forecast_horizon=self._forecast_horizon,
+            continuous=self._continuous)
         return data._into_dataloader_with_window_predict(
             self._window_size,
             self._forecast_horizon,
@@ -106,17 +101,13 @@ def _data_conversion_predict(self, input_data: Table | TimeSeriesDataset, batch_
 
     def _data_conversion_output(
         self,
-        input_data: Table | TimeSeriesDataset,
+        input_data: Table,
         output_data: Tensor,
     ) -> TimeSeriesDataset:
         table_data: Table
         window_size: int = self._window_size
         forecast_horizon: int = self._forecast_horizon
-        if isinstance(input_data, TimeSeriesDataset):
-            table_data = input_data.to_table()
-        else:
-            table_data = input_data
-
+        table_data = input_data
         input_data_table = table_data.slice_rows(start=window_size + forecast_horizon)
 
         return input_data_table.replace_column(
@@ -142,18 +133,11 @@ def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
         return (sorted(input_data.features.column_names).__eq__(
             sorted(self._feature_names), ) and input_data.target.name == self._target_name)
 
-    def _is_predict_data_valid(self, input_data: Table | TimeSeriesDataset) -> bool:
-        if isinstance(input_data, Table):
-            for name in self._feature_names:
-                if name not in input_data.column_names:
-                    return False
-            if self._target_name not in input_data.column_names:
+    def _is_predict_data_valid(self, input_data: Table) -> bool:
+        for name in self._feature_names:
+            if name not in input_data.column_names:
                 return False
-            return True
-        if isinstance(input_data, TimeSeriesDataset):
-            return (self._target_name == input_data.target.name
-                    and self._feature_names == input_data.features.column_names
-                    and self._extra_names == input_data.extras.column_names
-                    and self._forecast_horizon == input_data.forecast_horizon
-                    and self._window_size == input_data.window_size
-                    and self._continuous == input_data.continuous)
+        if self._target_name not in input_data.column_names:
+            return False
+        return True
+
diff --git a/tests/safeds/ml/nn/test_lstm_workflow.py b/tests/safeds/ml/nn/test_lstm_workflow.py
index 64473995c..2727c5817 100644
--- a/tests/safeds/ml/nn/test_lstm_workflow.py
+++ b/tests/safeds/ml/nn/test_lstm_workflow.py
@@ -59,12 +59,6 @@ def test_lstm_model(device: Device) -> None:
         epoch_size=1,
     )
 
-    trained_model_2.predict(test_table.to_time_series_dataset(
-            "value",
-            window_size=7,
-            forecast_horizon=12,
-            continuous=False,
-            extra_names=["date"],
-        ))
+    trained_model_2.predict(test_table)
     assert trained_model._model is not None
     assert trained_model._model.state_dict()["_pytorch_layers.0._layer.weight"].device == _get_device()

From a155ad00cdf4ecdfd836bec2af625c43fc775903 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 20 Jun 2024 19:10:11 +0200
Subject: [PATCH 13/15] fixed linter

---
 src/safeds/ml/nn/_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py
index a36bf0195..3d9086d61 100644
--- a/src/safeds/ml/nn/_model.py
+++ b/src/safeds/ml/nn/_model.py
@@ -6,7 +6,7 @@
 from safeds._config import _init_default_device
 from safeds._validation import _check_bounds, _ClosedBound
 from safeds.data.image.containers import ImageList
-from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset, Dataset
+from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset
 from safeds.data.labeled.containers._image_dataset import _ColumnAsTensor
 from safeds.data.tabular.containers import Table
 from safeds.data.tabular.transformation import OneHotEncoder
@@ -258,7 +258,7 @@ def fit(
         copied_model._model.eval()
         return copied_model
 
-    def predict(self, test_data: IPT | Dataset) -> IFT:
+    def predict(self, test_data: IPT) -> IFT:
         """
         Make a prediction for the given test data.
 
@@ -562,7 +562,7 @@ def fit(
         copied_model._model.eval()
         return copied_model
 
-    def predict(self, test_data: IPT | Dataset) -> IFT:
+    def predict(self, test_data: IPT) -> IFT:
         """
         Make a prediction for the given test data.
 

From d26d9d3f86c64462235d86ad5254d589dd91ed9b Mon Sep 17 00:00:00 2001
From: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Date: Thu, 20 Jun 2024 17:11:45 +0000
Subject: [PATCH 14/15] style: apply automated linter fixes

---
 .../ml/nn/converters/_input_converter_time_series.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

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 52ce3023e..110bbd3a2 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -92,7 +92,8 @@ def _data_conversion_predict(self, input_data: Table, batch_size: int) -> DataLo
             window_size=self._window_size,
             extra_names=self._extra_names,
             forecast_horizon=self._forecast_horizon,
-            continuous=self._continuous)
+            continuous=self._continuous,
+        )
         return data._into_dataloader_with_window_predict(
             self._window_size,
             self._forecast_horizon,
@@ -130,8 +131,12 @@ def _is_fit_data_valid(self, input_data: TimeSeriesDataset) -> bool:
             self._continuous = input_data._continuous
             self._first = False
             self._extra_names = input_data.extras.column_names
-        return (sorted(input_data.features.column_names).__eq__(
-            sorted(self._feature_names), ) and input_data.target.name == self._target_name)
+        return (
+            sorted(input_data.features.column_names).__eq__(
+                sorted(self._feature_names),
+            )
+            and input_data.target.name == self._target_name
+        )
 
     def _is_predict_data_valid(self, input_data: Table) -> bool:
         for name in self._feature_names:
@@ -140,4 +145,3 @@ def _is_predict_data_valid(self, input_data: Table) -> bool:
         if self._target_name not in input_data.column_names:
             return False
         return True
-

From 8469ab9f42388d68467e38c088e21fb3a3343dab Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Thu, 27 Jun 2024 16:53:19 +0200
Subject: [PATCH 15/15] added extranames to eq hash and size_of

---
 src/safeds/ml/nn/converters/_input_converter_time_series.py | 3 +++
 1 file changed, 3 insertions(+)

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 110bbd3a2..08e166f19 100644
--- a/src/safeds/ml/nn/converters/_input_converter_time_series.py
+++ b/src/safeds/ml/nn/converters/_input_converter_time_series.py
@@ -38,6 +38,7 @@ def __eq__(self, other: object) -> bool:
             and self._target_name == other._target_name
             and self._feature_names == other._feature_names
             and self._continuous == other._continuous
+            and self._extra_names == other._extra_names
         )
 
     def __hash__(self) -> int:
@@ -48,6 +49,7 @@ def __hash__(self) -> int:
             self._target_name,
             self._feature_names,
             self._continuous,
+            self._extra_names,
         )
 
     def __sizeof__(self) -> int:
@@ -57,6 +59,7 @@ def __sizeof__(self) -> int:
             + sys.getsizeof(self._target_name)
             + sys.getsizeof(self._feature_names)
             + sys.getsizeof(self._continuous)
+            + sys.getsizeof(self._extra_names)
         )
 
     @property