diff --git a/docs/source/notebooks/usage.ipynb b/docs/source/notebooks/usage.ipynb
index 3f217a0..20e7e15 100644
--- a/docs/source/notebooks/usage.ipynb
+++ b/docs/source/notebooks/usage.ipynb
@@ -9,17 +9,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           x1        x2        x3\n",
+      "0    1.764052  0.555963 -1.532921\n",
+      "1    0.400157  0.892474 -1.711970\n",
+      "2    0.978738 -0.422315  0.046135\n",
+      "3    2.240893  0.104714 -0.958374\n",
+      "4    1.867558  0.228053 -0.080812\n",
+      "..        ...       ...       ...\n",
+      "995  0.412871  0.097751  2.079177\n",
+      "996 -0.198399  1.401523 -0.907466\n",
+      "997  0.094192  0.158434 -0.192404\n",
+      "998 -1.147611 -1.141901 -1.212516\n",
+      "999 -0.358114 -1.310970 -0.080599\n",
+      "\n",
+      "[1000 rows x 3 columns]\n",
+      "0      1.400443\n",
+      "1      1.424361\n",
+      "2      0.873649\n",
+      "3      2.523680\n",
+      "4      0.276468\n",
+      "         ...   \n",
+      "995    2.488424\n",
+      "996    1.042324\n",
+      "997    0.989404\n",
+      "998   -3.272610\n",
+      "999   -1.335277\n",
+      "Name: y, Length: 1000, dtype: float64\n",
+      "['x1', 'x2', 'x3']\n"
+     ]
+    }
+   ],
    "source": [
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import tefs\n",
+    "\n",
+    "# Generate some random data\n",
+    "import numpy as np\n",
+    "np.random.seed(0)\n",
+    "n = 1000\n",
+    "x1 = np.random.normal(size=n)\n",
+    "x2 = np.random.normal(size=n)\n",
+    "x3 = np.random.normal(size=n)\n",
+    "x4 = x1 + x2 + x3 + np.random.normal(size=n)\n",
+    "y = x1 + x2 + x3 + np.random.normal(size=n)\n",
+    "data = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3, 'x4': x4, 'y': y})\n",
+    "\n",
+    "# Define features and target\n",
+    "features = data[['x1', 'x2', 'x3']]\n",
+    "target = data['y']\n",
+    "features_names = list(features.columns)\n",
+    "\n",
+    "# Set k using the rule of thumb\n",
+    "n_samples = len(data)\n",
+    "k = n_samples // 10\n",
+    "direction = 'forward' # or 'backward'\n",
+    "lag_features = 1\n",
+    "lag_target = 1\n",
+    "\n",
+    "print(features)\n",
+    "print(target)\n",
+    "print(features_names)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding feature: x2 with TE score: -0.002435012237684899\n",
+      "Adding feature: x1 with TE score: -0.0015847873844891665\n",
+      "Adding feature: x3 with TE score: -0.001428146893958049\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Perform feature selection\n",
+    "result = tefs.fs(\n",
+    "    features=features.values,\n",
+    "    target=target.values,\n",
+    "    k=k,\n",
+    "    direction=direction,\n",
+    "    lag_features=lag_features,\n",
+    "    lag_target=lag_target,\n",
+    "    verbose=1,\n",
+    "    var_names=features_names, # optional\n",
+    "    n_jobs=4,\n",
+    ")"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,