diff --git a/examples/TimeSeries.ipynb b/examples/TimeSeries.ipynb
index 97c18a5..66f1495 100644
--- a/examples/TimeSeries.ipynb
+++ b/examples/TimeSeries.ipynb
@@ -1,12 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Requirements: data from https://archive.ics.uci.edu/ml/machine-learning-databases/00396/Sales_Transactions_Dataset_Weekly.csv"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -28,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -54,9 +47,9 @@
    "source": [
     "%autoreload 2\n",
     "import pandas as pd\n",
-    "# data from\n",
-    "# https://archive.ics.uci.edu/ml/datasets/Sales_Transactions_Dataset_Weekly\n",
-    "sales_transaction = pd.read_csv('Sales_Transactions_Dataset_Weekly.csv')\n",
+    "\n",
+    "data_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00396/Sales_Transactions_Dataset_Weekly.csv'\n",
+    "sales_transaction = pd.read_csv(data_url)\n",
     "data = sales_transaction[[f'Normalized {i}' for i in range(52)]].values\n",
     "som = MiniSom(8, 8, data.shape[1], sigma=2., learning_rate=0.5, \n",
     "              neighborhood_function='gaussian', random_seed=10)\n",
@@ -68,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {