diff --git a/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb b/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb
index e35751ffc..56e94979e 100644
--- a/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb	
+++ b/notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb	
@@ -181,7 +181,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our `(AAPL, 2015-07-15 00:00:00)` is also in the data again:"
+    "Our `(AAPL, 2020-07-14 00:00:00)` is also in the data again:"
    ]
   },
   {
@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X.loc[\"AAPL\", pd.to_datetime('2015-07-15')]"
+    "X.loc[\"AAPL\", pd.to_datetime('2020-07-14')]"
    ]
   },
   {
@@ -242,7 +242,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y[\"AAPL\", pd.to_datetime(\"2015-07-15\")], df.loc[pd.to_datetime(\"2015-07-16\"), \"AAPL\"]"
+    "y[\"AAPL\", pd.to_datetime(\"2020-07-13\")], df.loc[pd.to_datetime(\"2020-07-14\"), \"AAPL\"]"
    ]
   },
   {
@@ -354,7 +354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/examples/05 Timeseries Forecasting.ipynb b/notebooks/examples/05 Timeseries Forecasting.ipynb
index 3d213cd44..37e21f687 100644
--- a/notebooks/examples/05 Timeseries Forecasting.ipynb	
+++ b/notebooks/examples/05 Timeseries Forecasting.ipynb	
@@ -159,7 +159,7 @@
    "metadata": {},
    "source": [
     "The resulting dataframe now consists of these \"windows\" stamped out of the original dataframe.\n",
-    "For example all data with the `id = (AAPL, 2015-07-15 00:00:00)` comes from the original data of stock `AAPL` including the last 20 days until `2015-07-15`:"
+    "For example all data with the `id = (AAPL, 2020-07-14 00:00:00)` comes from the original data of stock `AAPL` including the last 20 days until `2020-07-14`:"
    ]
   },
   {
@@ -168,7 +168,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_rolled[df_rolled[\"id\"] == (\"AAPL\", pd.to_datetime(\"2015-07-15\"))]"
+    "df_rolled[df_rolled[\"id\"] == (\"AAPL\", pd.to_datetime(\"2020-07-14\"))]"
    ]
   },
   {
@@ -177,8 +177,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_melted[(df_melted[\"date\"] <= pd.to_datetime(\"2015-07-15\")) & \n",
-    "          (df_melted[\"date\"] >= pd.to_datetime(\"2015-06-16\")) & \n",
+    "df_melted[(df_melted[\"date\"] <= pd.to_datetime(\"2020-07-14\")) & \n",
+    "          (df_melted[\"date\"] >= pd.to_datetime(\"2020-06-15\")) & \n",
     "          (df_melted[\"Symbols\"] == \"AAPL\")]"
    ]
   },
@@ -260,7 +260,7 @@
     "The rolled (windowed) data sample is now in the correct format to use it for `tsfresh`s feature extraction.\n",
     "As normal, features will be extracted using all data for a given `id`, which is in our case all data of a given window and a given id (one colored box in the graph above).\n",
     "\n",
-    "If the feature extraction will return a row with the index `(AAPL, 2015-07-15 00:00:00)`, you now it has been calculated using the `AAPL` data up and including `2015-07-15` (and 20 days of history)."
+    "If the feature extraction will return a row with the index `(AAPL, 2020-07-14 00:00:00)`, you now it has been calculated using the `AAPL` data up and including `2020-07-14` (and 20 days of history)."
    ]
   },
   {
@@ -307,7 +307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our `(AAPL, 2015-07-15 00:00:00)` is also in the data again:"
+    "Our `(AAPL, 2020-07-14 00:00:00)` is also in the data again:"
    ]
   },
   {
@@ -316,7 +316,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X.loc['2015-07-15']"
+    "X.loc['2020-07-14']"
    ]
   },
   {
@@ -339,7 +339,7 @@
    "source": [
     "We can now use the extracted features to train a regressor.\n",
     "But what will be our targets?\n",
-    "The target for the row `2015-07-15` is the value on the next timestep (that would be `2015-07-16` in this case).\n",
+    "The target for the row `2020-07-13` is the value on the next timestep (that would be `2020-07-14` in this case).\n",
     "\n",
     "So all we need to do is go back to our original dataframe and take the stock value of tomorrow.\n",
     "This is done with `shift`:"
@@ -367,14 +367,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y[\"2015-07-15\"], df[\"2015-07-16\"].iloc[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The target for `2015-07-15` is to forecast the value of the next day, `2015-07-16`."
+    "y[\"2020-07-13\"], df[\"2020-07-14\"].iloc[0]"
    ]
   },
   {
@@ -514,7 +507,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,