Add code examples in timeseries module's docstrings (#647)

* Adding code examples for Timeseries Module

* Fixing details in the examples

Author: datacubeR

feature_engine/timeseries/forecasting/expanding_window_features.py

@@ -117,6 +117,28 @@ class ExpandingWindowFeatures(BaseForecastTransformer):
     pandas.expanding
     pandas.aggregate
     pandas.shift
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.timeseries.forecasting import ExpandingWindowFeatures
+    >>> X = pd.DataFrame(dict(date = ["2022-09-18",
+    >>>                               "2022-09-19",
+    >>>                               "2022-09-20",
+    >>>                               "2022-09-21",
+    >>>                               "2022-09-22"],
+    >>>                       x1 = [1,2,3,4,5],
+    >>>                       x2 = [6,7,8,9,10]
+    >>>                     ))
+    >>> ewf = ExpandingWindowFeatures()
+    >>> ewf.fit_transform(X)
+             date  x1  x2  x1_expanding_mean  x2_expanding_mean
+    0  2022-09-18   1   6                NaN                NaN
+    1  2022-09-19   2   7                1.0                6.0
+    2  2022-09-20   3   8                1.5                6.5
+    3  2022-09-21   4   9                2.0                7.0
+    4  2022-09-22   5  10                2.5                7.5
     """
 
     def __init__(

feature_engine/timeseries/forecasting/lag_features.py

@@ -95,6 +95,28 @@ class LagFeatures(BaseForecastTransformer):
     See Also
     --------
     pandas.shift
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.timeseries.forecasting import LagFeatures
+    >>> X = pd.DataFrame(dict(date = ["2022-09-18",
+    >>>                               "2022-09-19",
+    >>>                               "2022-09-20",
+    >>>                               "2022-09-21",
+    >>>                               "2022-09-22"],
+    >>>                       x1 = [1,2,3,4,5],
+    >>>                       x2 = [6,7,8,9,10]
+    >>>                     ))
+    >>> lf = LagFeatures(periods=[1,2])
+    >>> lf.fit_transform(X)
+                date  x1  x2  x1_lag_1  x2_lag_1  x1_lag_2  x2_lag_2
+    0  2022-09-18   1   6       NaN       NaN       NaN       NaN
+    1  2022-09-19   2   7       1.0       6.0       NaN       NaN
+    2  2022-09-20   3   8       2.0       7.0       1.0       6.0
+    3  2022-09-21   4   9       3.0       8.0       2.0       7.0
+    4  2022-09-22   5  10       4.0       9.0       3.0       8.0
     """
 
     def __init__(

feature_engine/timeseries/forecasting/window_features.py

@@ -121,6 +121,28 @@ class WindowFeatures(BaseForecastTransformer):
     pandas.rolling
     pandas.aggregate
     pandas.shift
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.timeseries.forecasting import WindowFeatures
+    >>> X = pd.DataFrame(dict(date = ["2022-09-18",
+    >>>                               "2022-09-19",
+    >>>                               "2022-09-20",
+    >>>                               "2022-09-21",
+    >>>                               "2022-09-22"],
+    >>>                       x1 = [1,2,3,4,5],
+    >>>                       x2 = [6,7,8,9,10]
+    >>>                     ))
+    >>> wf = WindowFeatures(window = 2)
+    >>> wf.fit_transform(X)
+             date  x1  x2  x1_window_2_mean  x2_window_2_mean
+    0  2022-09-18   1   6               NaN               NaN
+    1  2022-09-19   2   7               NaN               NaN
+    2  2022-09-20   3   8               1.5               6.5
+    3  2022-09-21   4   9               2.5               7.5
+    4  2022-09-22   5  10               3.5               8.5
     """
 
     def __init__(