Tweak dosctrings

pyriodicity/detectors/acf.py

@@ -26,8 +26,8 @@ class ACFPeriodicityDetector:
 
     Examples
     --------
-    Start by loading a timeseries datasets and resampling to an appropriate
-    frequency.
+    Start by loading Mauna Loa Weekly Atmospheric CO2 Data from Statsmodels
+    and downsampling its data to a monthly frequency.
 
     >>> from statsmodels.datasets import co2
     >>> data = co2.load().data

pyriodicity/detectors/autoperiod.py

@@ -32,8 +32,8 @@ class Autoperiod:
 
     Examples
     --------
-    Start by loading a timeseries datasets and resampling to an appropriate
-    frequency.
+    Start by loading Mauna Loa Weekly Atmospheric CO2 Data from Statsmodels
+    and downsampling its data to a monthly frequency.
 
     >>> from statsmodels.datasets import co2
     >>> data = co2.load().data

pyriodicity/detectors/cfd_autoperiod.py

@@ -11,7 +11,7 @@ class CFDAutoperiod:
     """
     CFD-Autoperiod periodicity detector.
 
-    Find the periods in a given signal or series using CFDAutoperiod [1]_.
+    Find the periods in a given signal or series using CFD-Autoperiod [1]_.
 
     Parameters
     ----------
@@ -33,8 +33,8 @@ class CFDAutoperiod:
 
     Examples
     --------
-    Start by loading a timeseries datasets and resampling to an appropriate
-    frequency.
+    Start by loading Mauna Loa Weekly Atmospheric CO2 Data from Statsmodels
+    and downsampling its data to a monthly frequency.
 
     >>> from statsmodels.datasets import co2
     >>> data = co2.load().data
@@ -59,9 +59,9 @@ class CFDAutoperiod:
     >>> cfd_autoperiod.fit(k=300)
     array([12])
 
-    ``CFDAutoperiod`` is considered a more robust variant of ``Autoperiod``.
-    The detection algorithm found exactly one periodicity of 12, suggesting
-    a strong yearly periodicity.
+    ``CFDAutoperiod`` is considered a more robust variant of ``Autoperiod``
+    against noise. The detection algorithm found exactly one periodicity
+    length of 12, suggesting a strong yearly periodicity.
     """
 
     def __init__(self, endog: ArrayLike):

pyriodicity/detectors/fft.py

@@ -45,10 +45,10 @@ class FFTPeriodicityDetector:
         40,  28,  20,   3,   5,  15,  29,  22,   2,  24,  53,  33,  26,
         16,  17,  21])
 
-    ``FFTPeriodicityDetector`` tends to be quite sensitive to noise and find
-    many false period lengths. Depending on your data, you can choose to
-    apply a window function to get different results. You can also limit the
-    number returned period length values to the 3 most signficant ones.
+    ``FFTPeriodicityDetector`` tends to be quite sensitive to noise and can find
+    many false period lengths. Depending on your data, you can choose to apply
+    a window function to get different results. You can also limit the number
+    returned period length values to the 3 most signficant ones.
 
     >>> fft_detector.fit(window_func="blackman", max_period_count=3)
     array([12, 13, 11])