diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9b921ad3de..10969f7695 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,6 +18,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Fixed**
 
+- Fixed `examples/14-transfer-learning.ipynb` raising a NaN error on the `air` dataset by calling `fill_missing_values(..., fill="auto")` to handle residual gaps in the raw `carrier_passengers.csv`. Closes [#3116](https://github.com/unit8co/darts/pull/3116) by [Jean-Baptiste Braun](https://github.com/jbbqqf).
 - Fixed `_ScaledDotProductAttention` float16 overflow in `masked_fill` under mixed precision training. [#3087](https://github.com/unit8co/darts/pull/3087) by [Robert Ruidisch](https://github.com/robrui).
 - Fixed a bug in `TimeSeries.quantile()` where the output dtype did not match the input series dtype for dtypes `float32` or `float16`. Now the dtype is correctly propagated. [#3124](https://github.com/unit8co/darts/pull/3124) by [Dennis Bader](https://github.com/dennisbader)
 - Optuna integration's `PyTorchLightningPruningCallback` for hyperparameter optimization of torch models is now natively available in Darts via `darts.utils.callbacks`. [#3114](https://github.com/unit8co/darts/pull/3114) by [Jakub Chłapek](https://github.com/jakubchlapek).
diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb
index 579e6a5521..b2a5c4b642 100644
--- a/examples/14-transfer-learning.ipynb
+++ b/examples/14-transfer-learning.ipynb
@@ -130,7 +130,8 @@
     "    RandomForestModel,\n",
     "    Theta,\n",
     ")\n",
-    "from darts.utils.losses import SmapeLoss"
+    "from darts.utils.losses import SmapeLoss\n",
+    "from darts.utils.missing_values import fill_missing_values"
    ]
   },
   {
@@ -244,6 +245,8 @@
     "            series = series.longest_contiguous_slice()\n",
     "        except Exception:\n",
     "            continue\n",
+    "        # fill missing values.\n",
+    "        series = fill_missing_values(series, fill=\"auto\")\n",
     "        # remove static covariates\n",
     "        series = series.with_static_covariates(None)\n",
     "        # remove short series\n",