make titles consistent across notebooks

Author: aagrande

examples/README_example.ipynb

@@ -4,6 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Quick start: Forecasting with synthetic data\n",
+    "\n",
     "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures the underlying distribution of the data."
    ]
   },

examples/m5-news-vendor-notebook.ipynb

@@ -4,14 +4,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Forecasting Walmart sales with Treeffuser\n",
+    "\n",
     "In this tutorial we show how to use Treeffuser to model and forecast Walmart sales using the M5 forecasting dataset from Kaggle."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Getting started\n"
+    "## Getting started\n"
    ]
   },
   {
@@ -128,9 +130,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# The data\n",
+    "## The data\n",
     "\n",
-    "## Preprocessing\n",
+    "### Preprocessing\n",
     "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time."
    ]
   },
@@ -786,7 +788,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Train and test sets"
+    "### Train and test sets"
    ]
   },
   {
@@ -1140,7 +1142,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Probabilistic predictions with Treeffuser\n",
+    "## Probabilistic predictions with Treeffuser\n",
     "\n",
     "We regress the sales on the following covariates."
    ]