diff --git a/book/machinelearning/timeseries.ipynb b/book/machinelearning/timeseries.ipynb
index 16a0af1..d257926 100644
--- a/book/machinelearning/timeseries.ipynb
+++ b/book/machinelearning/timeseries.ipynb
@@ -307,6 +307,62 @@
     "\n",
     "scores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series Forecasting with Deep Learning with `neuralforecast`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do you want to perform powerful time series forecasting?\n",
+    "\n",
+    "Try `neuralforecast` by nixtla.\n",
+    "\n",
+    "`neuralforecast` lets you run Deep Learning models for time series forecasting with models like N-BEATS or N-HiTS.\n",
+    "\n",
+    "Support for exogenous variables and probabilistic forecasting are also included.\n",
+    "\n",
+    "Check the example below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install neuralforecast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from neuralforecast import NeuralForecast\n",
+    "from neuralforecast.models import NBEATS, NHITS\n",
+    "from neuralforecast.utils import AirPassengersDF\n",
+    "\n",
+    "Y_df = AirPassengersDF\n",
+    "Y_train_df = Y_df[Y_df.ds<='1959-12-31']\n",
+    "Y_test_df = Y_df[Y_df.ds>'1959-12-31']\n",
+    "\n",
+    "horizon = 12\n",
+    "models = [NBEATS(input_size=2 * horizon, h=horizon, max_steps=50),\n",
+    "          NHITS(input_size=2 * horizon, h=horizon, max_steps=50)]\n",
+    "\n",
+    "nf = NeuralForecast(models=models, freq='M')\n",
+    "nf.fit(df=Y_train_df)\n",
+    "Y_hat_df = nf.predict().reset_index()"
+   ]
   }
  ],
  "metadata": {