diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 73f248b..7d7c7cf 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -3,34 +3,33 @@ name: deploy-book
 on:
   push:
     branches:
-      - "*"
+      - '*'
 
 # This job installs dependencies, build the book, and pushes it to `gh-pages`
 jobs:
   deploy-book:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@v2
 
-    # Install dependencies
-    - name: Set up Python 3.7
-      uses: actions/setup-python@v1
-      with:
-        python-version: 3.7
+      # Install dependencies
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.7
 
-    - name: Install dependencies
-      run: |
-        pip install -r requirements.txt
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt
 
+      # Build the book
+      - name: Build the book
+        run: |
+          jupyter-book build .
 
-    # Build the book
-    - name: Build the book
-      run: |
-        jupyter-book build .
-
-    # Push the book's HTML to github-pages
-    - name: GitHub Pages action
-      uses: peaceiris/actions-gh-pages@v3.5.9
-      with:
-        github_token: ${{ secrets.ACCESS_TOKEN }}
-        publish_dir: ./_build/html
+      # Push the book's HTML to github-pages
+      - name: GitHub Pages action
+        uses: peaceiris/actions-gh-pages@v3.5.9
+        with:
+          github_token: ${{ secrets.ACCESS_TOKEN }}
+          publish_dir: ./_build/html
diff --git a/book/machinelearning/timeseries.ipynb b/book/machinelearning/timeseries.ipynb
index ec94296..16a0af1 100644
--- a/book/machinelearning/timeseries.ipynb
+++ b/book/machinelearning/timeseries.ipynb
@@ -252,6 +252,61 @@
     "sf.fit(df)\n",
     "sf.predict(h=12, level=[95])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series with Polars Backend with `functime`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fast time-series forecasting with `functime`.\n",
+    "\n",
+    "`functime` is a Python library for time series forecasting and feature extraction, built with Polars.\n",
+    "\n",
+    "Since it uses lazy Polars dataframes, `functime` speeds up forecasting and feature engineering.\n",
+    "\n",
+    "Backtesting, cross-validation splitters and metrics are included too.\n",
+    "\n",
+    "It even comes with a LLM agent to analyze and describe your forecasts.\n",
+    "\n",
+    "Check it out!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install functime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "from functime.cross_validation import train_test_split\n",
+    "from functime.forecasting import linear_model\n",
+    "from functime.metrics import mase\n",
+    "\n",
+    "y_train, y_test = y.pipe(train_test_split(test_size=3))\n",
+    "\n",
+    "forecaster = linear_model(freq=\"1mo\", lags=24)\n",
+    "forecaster.fit(y=y_train)\n",
+    "y_pred = forecaster.predict(fh=3)\n",
+    "\n",
+    "y_pred = linear_model(freq=\"1mo\", lags=24)(y=y_train, fh=3)\n",
+    "\n",
+    "scores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)"
+   ]
   }
  ],
  "metadata": {