diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index 5e6761771..e86234e02 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -2206,23 +2206,33 @@
     "#| polars\n",
     "models = [LSTM(h=12, input_size=24, max_steps=5, hist_exog_list=['zeros'], scaler_type='robust')]\n",
     "nf = NeuralForecast(models=models, freq='M')\n",
-    "nf.fit(AirPassengersPanel_train)\n",
+    "nf.fit(AirPassengersPanel_train, static_df=AirPassengersStatic)\n",
     "insample_preds = nf.predict_insample()\n",
     "preds = nf.predict()\n",
-    "cv_res = nf.cross_validation(df=AirPassengersPanel_train)\n",
+    "cv_res = nf.cross_validation(df=AirPassengersPanel_train, static_df=AirPassengersStatic)\n",
     "\n",
     "renamer = {'unique_id': 'uid', 'ds': 'time', 'y': 'target'}\n",
     "inverse_renamer = {v: k for k, v in renamer.items()}\n",
     "AirPassengers_pl = polars.from_pandas(AirPassengersPanel_train)\n",
     "AirPassengers_pl = AirPassengers_pl.rename(renamer)\n",
+    "AirPassengersStatic_pl = polars.from_pandas(AirPassengersStatic)\n",
+    "AirPassengersStatic_pl = AirPassengersStatic_pl.rename({'unique_id': 'uid'})\n",
     "nf = NeuralForecast(models=models, freq='1mo')\n",
     "nf.fit(\n",
-    "    AirPassengers_pl, id_col='uid', time_col='time', target_col='target'\n",
+    "    AirPassengers_pl,\n",
+    "    static_df=AirPassengersStatic_pl,\n",
+    "    id_col='uid',\n",
+    "    time_col='time',\n",
+    "    target_col='target',\n",
     ")\n",
     "insample_preds_pl = nf.predict_insample()\n",
     "preds_pl = nf.predict()\n",
     "cv_res_pl = nf.cross_validation(\n",
-    "    df=AirPassengers_pl, id_col='uid', time_col='time', target_col='target'\n",
+    "    df=AirPassengers_pl,\n",
+    "    static_df=AirPassengersStatic_pl,\n",
+    "    id_col='uid',\n",
+    "    time_col='time',\n",
+    "    target_col='target',\n",
     ")\n",
     "\n",
     "def assert_equal_dfs(pandas_df, polars_df):\n",