Expand the example page

docs/example.rst

@@ -1,7 +1,6 @@
 Example
 -------
-
-Start by loading Mauna Loa Weekly Atmospheric CO2 Data from `statsmodels <https://www.statsmodels.org>`__
+For this example, start by loading Mauna Loa Weekly Atmospheric CO2 Data from `statsmodels <https://www.statsmodels.org>`__
 and downsampling its data to a monthly frequency.
 
 .. code:: python
@@ -22,7 +21,25 @@ Use ``Autoperiod`` to find the list of periods based in this data (if any).
 The detected periodicity length is 12 which suggests a strong yearly
 seasonality given that the data has a monthly frequency.
 
+To confirm this, we run the ``Autoperiod`` again on the same data resampled to a weekly and daily frequencies.
+
+.. code:: python
+
+   >>> data = co2.load().data
+   >>> data = data.resample("W").mean().ffill()
+   >>> autoperiod = Autoperiod(data)
+   >>> autoperiod.fit()
+   array([52])
+   >>> data = co2.load().data
+   >>> data = data.resample("D").mean().ffill()
+   >>> autoperiod = Autoperiod(data)
+   >>> autoperiod.fit()
+   array([364])
+
+As expected, the detected period lengths for weekly and daily frequencies are 52 and 364, respectively,
+which confirms the strong yearly seasonality of the data.
+
 You can use other estimation algorithms like ``CFDAutoperiod`` variant of ``Autoperiod``,
 ``ACFPeriodicityDetector``, or ``FFTPeriodicityDetector`` and compare results and performance.
-All estimation algorithms can be used in the same manner as in the example above with different
-optional parameters. Check the :doc:`api` for more details.
+All the supported estimation algorithms can be used in the same manner as in the example above
+with different optional parameters. Check the :doc:`api` for more details.