use ffill in forecasting example

notebook/automl_time_series_forecast.ipynb

@@ -152,13 +152,11 @@
    "outputs": [],
    "source": [
     "import statsmodels.api as sm\n",
-    "data = sm.datasets.co2.load_pandas()\n",
-    "data = data.data\n",
+    "data = sm.datasets.co2.load_pandas().data\n",
     "# data is given in weeks, but the task is to predict monthly, so use monthly averages instead\n",
     "data = data['co2'].resample('MS').mean()\n",
-    "data = data.fillna(data.bfill())  # makes sure there are no missing values\n",
-    "data = data.to_frame().reset_index()\n",
-    "# data = data.rename(columns={'index': 'ds', 'co2': 'y'})"
+    "data = data.bfill().ffill()  # makes sure there are no missing values\n",
+    "data = data.to_frame().reset_index()"
    ]
   },
   {

test/automl/test_forecast.py

@@ -8,7 +8,8 @@ def test_forecast_automl(budget=5):
 
     data = sm.datasets.co2.load_pandas().data["co2"].resample("MS").mean()
     data = (
-        data.fillna(data.bfill())
+        data.bfill()
+        .ffill()
         .to_frame()
         .reset_index()
         .rename(columns={"index": "ds", "co2": "y"})

website/docs/Examples/AutoML-Time series forecast.md

@@ -247,7 +247,7 @@ import statsmodels.api as sm
 data = sm.datasets.co2.load_pandas().data
 # data is given in weeks, but the task is to predict monthly, so use monthly averages instead
 data = data['co2'].resample('MS').mean()
-data = data.fillna(data.bfill())  # makes sure there are no missing values
+data = data.bfill().ffill()  # makes sure there are no missing values
 data = data.to_frame().reset_index()
 num_samples = data.shape[0]
 time_horizon = 12