WindowFeatures#
WindowFeatures adds window features to the dataframe. Window features are
the result of window operations over the variables. Window operations are operations that
perform an aggregation over a sliding partition of past values. A window feature is,
then, a feature created after computing mathematical functions (e.g., mean, min,
max, etc.) within a window over the past data.
For example, the mean value of the previous 3 months of data is a window feature. The maximum value of the previous three rows of data is another window feature.
When forecasting the future values of a variable, the past values of that variable are
likely to be predictive. To capitalize on the past values of a variable, we can simply
lag features with LagFeatures. And, we can as well create features that
take in consideration the values in the past but within a window.
To create window features we need to determine a number of parameters. First, we need to identify the size of the window or windows in which we will perform the operations. For example, we can take the average of the variable over 3 months, or 6 weeks. We also need to determine how far back is the window located respect to the value we want to forecast. For example, I can take the average of the last 3 weeks of data to forecast this week of data, or I can take the average of the last 3 weeks of data to forecast next weeks data, leaving a gap of a window in between the window feature and the forecasting point.
WindowFeatures transformer works on top of pandas.rolling, pandas.aggregate
and pandas.shift. With pandas.rolling, WindowFeatures determines the size
of the windows for the operations. With pandas.rolling we can specify the window size
with an integer, a string or a function. With WindowFeatures, in addition, we
can pass a list of integers, strings or functions, to perform computations over multiple
window sizes.
WindowFeatures uses pandas.aggregate to perform the mathematical operations
over the windows. Therefore, you can use any operation supported
by pandas. For supported aggregation functions, see Rolling Window
Functions.
With pandas.shift, WindowFeatures places the value derived from the past
window, at the place of the value that we want to forecast. So if we want to forecast
this week with the average of the past 3 weeks of data, we should shift the value 1
week forward. If we want to forecast next week with the last 3 weeks of data, we should
forward the value 2 weeks forward.
WindowFeatures will add the new variables with a representative name to the
original dataframe. It also has the methods fit() and transform() that make it
compatible with the Scikit-learn’s Pipeline and cross-validation functions.
Note that to be compatible with WindowFeatures the dataframe’s index must have
unique values and no NaN.
Examples#
Let’s create a toy dataset to demonstrate the functionality of WindowFeatures.
The dataframe contains 3 numerical variables, a categorical variable, and a datetime
index.
import pandas as pd
X = {"ambient_temp": [31.31, 31.51, 32.15, 32.39, 32.62, 32.5, 32.52, 32.68],
"module_temp": [49.18, 49.84, 52.35, 50.63, 49.61, 47.01, 46.67, 47.52],
"irradiation": [0.51, 0.79, 0.65, 0.76, 0.42, 0.49, 0.57, 0.56],
"color": ["green"] * 4 + ["blue"] * 4,
}
X = pd.DataFrame(X)
X.index = pd.date_range("2020-05-15 12:00:00", periods=8, freq="15min")
X.head()
Below we see the output of our toy dataframe:
ambient_temp module_temp irradiation color
2020-05-15 12:00:00 31.31 49.18 0.51 green
2020-05-15 12:15:00 31.51 49.84 0.79 green
2020-05-15 12:30:00 32.15 52.35 0.65 green
2020-05-15 12:45:00 32.39 50.63 0.76 green
2020-05-15 13:00:00 32.62 49.61 0.42 blue
Now we will create window features from the numerical variables. By setting
window=["30min", "60min"] we perform calculations over windows of 30 and 60
minutes, respectively. If you look at our toy dataframe, you’ll notice that 30 minutes
corresponds to 2 rows of data, and 60 minutes are 4 rows of data. So, we will perform calculations
over 2 and then 4 rows of data, respectively.
In functions, we indicate all the operations that we want to perform over those windows.
In our example below, we want to calculate the mean and the standard deviation of the
data within those windows and also find the maximum value within the windows.
With freq="15min" we indicate that we need to shift the calculations 15 minutes
forward. In other words, we are using the data available in windows defined up to 15 minutes
before the forecasting point.
from feature_engine.timeseries.forecasting import WindowFeatures
win_f = WindowFeatures(
window=["30min", "60min"], functions=["mean", "max", "std"], freq="15min",
)
X_tr = win_f.fit_transform(X)
X_tr.head()
We can find the window features on the right side of the dataframe.
ambient_temp module_temp irradiation color \
2020-05-15 12:00:00 31.31 49.18 0.51 green
2020-05-15 12:15:00 31.51 49.84 0.79 green
2020-05-15 12:30:00 32.15 52.35 0.65 green
2020-05-15 12:45:00 32.39 50.63 0.76 green
2020-05-15 13:00:00 32.62 49.61 0.42 blue
ambient_temp_window_30min_mean \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 31.31
2020-05-15 12:30:00 31.41
2020-05-15 12:45:00 31.83
2020-05-15 13:00:00 32.27
ambient_temp_window_30min_max \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 31.31
2020-05-15 12:30:00 31.51
2020-05-15 12:45:00 32.15
2020-05-15 13:00:00 32.39
ambient_temp_window_30min_std \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 0.141421
2020-05-15 12:45:00 0.452548
2020-05-15 13:00:00 0.169706
module_temp_window_30min_mean \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 49.180
2020-05-15 12:30:00 49.510
2020-05-15 12:45:00 51.095
2020-05-15 13:00:00 51.490
module_temp_window_30min_max \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 49.18
2020-05-15 12:30:00 49.84
2020-05-15 12:45:00 52.35
2020-05-15 13:00:00 52.35
module_temp_window_30min_std ... \
2020-05-15 12:00:00 NaN ...
2020-05-15 12:15:00 NaN ...
2020-05-15 12:30:00 0.466690 ...
2020-05-15 12:45:00 1.774838 ...
2020-05-15 13:00:00 1.216224 ...
irradiation_window_30min_std \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 0.197990
2020-05-15 12:45:00 0.098995
2020-05-15 13:00:00 0.077782
ambient_temp_window_60min_mean \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 31.310000
2020-05-15 12:30:00 31.410000
2020-05-15 12:45:00 31.656667
2020-05-15 13:00:00 31.840000
ambient_temp_window_60min_max \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 31.31
2020-05-15 12:30:00 31.51
2020-05-15 12:45:00 32.15
2020-05-15 13:00:00 32.39
ambient_temp_window_60min_std \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 0.141421
2020-05-15 12:45:00 0.438786
2020-05-15 13:00:00 0.512640
module_temp_window_60min_mean \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 49.180000
2020-05-15 12:30:00 49.510000
2020-05-15 12:45:00 50.456667
2020-05-15 13:00:00 50.500000
module_temp_window_60min_max \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 49.18
2020-05-15 12:30:00 49.84
2020-05-15 12:45:00 52.35
2020-05-15 13:00:00 52.35
module_temp_window_60min_std \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 0.466690
2020-05-15 12:45:00 1.672553
2020-05-15 13:00:00 1.368381
irradiation_window_60min_mean \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 0.5100
2020-05-15 12:30:00 0.6500
2020-05-15 12:45:00 0.6500
2020-05-15 13:00:00 0.6775
irradiation_window_60min_max \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 0.51
2020-05-15 12:30:00 0.79
2020-05-15 12:45:00 0.79
2020-05-15 13:00:00 0.79
irradiation_window_60min_std
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 0.197990
2020-05-15 12:45:00 0.140000
2020-05-15 13:00:00 0.126853
[5 rows x 22 columns]
The variables used as input for the window features are stored in the variables_
attribute of the WindowFeatures:
win_f.variables_
['ambient_temp', 'module_temp', 'irradiation']
We can obtain the names of the variables in the returned dataframe using the get_feature_names_out() method:
win_f.get_feature_names_out()
['ambient_temp',
'module_temp',
'irradiation',
'color',
'ambient_temp_window_30min_mean',
'ambient_temp_window_30min_max',
'ambient_temp_window_30min_std',
'module_temp_window_30min_mean',
'module_temp_window_30min_max',
'module_temp_window_30min_std',
'irradiation_window_30min_mean',
'irradiation_window_30min_max',
'irradiation_window_30min_std',
'ambient_temp_window_60min_mean',
'ambient_temp_window_60min_max',
'ambient_temp_window_60min_std',
'module_temp_window_60min_mean',
'module_temp_window_60min_max',
'module_temp_window_60min_std',
'irradiation_window_60min_mean',
'irradiation_window_60min_max',
'irradiation_window_60min_std']
We can get the names of the window features created for the variable “irradiation” as follows:
win_f.get_feature_names_out(["irradiation"])
['irradiation_window_30min_mean',
'irradiation_window_30min_max',
'irradiation_window_30min_std',
'irradiation_window_60min_mean',
'irradiation_window_60min_max',
'irradiation_window_60min_std']
Working with pandas series#
If your time series is a pandas Series instead of a pandas Dataframe, you need to
transform it into a dataframe before using WindowFeatures.
The following is a pandas Series:
X['ambient_temp']
2020-05-15 12:00:00 31.31
2020-05-15 12:15:00 31.51
2020-05-15 12:30:00 32.15
2020-05-15 12:45:00 32.39
2020-05-15 13:00:00 32.62
2020-05-15 13:15:00 32.50
2020-05-15 13:30:00 32.52
2020-05-15 13:45:00 32.68
Freq: 15T, Name: ambient_temp, dtype: float64
We can use WindowFeatures to create, for example, 2 new window features by finding
the mean and maximum value within a 45 minute windows of a pandas Series if we convert it
to a pandas Dataframe using the method to_frame():
win_f = WindowFeatures(
window=["45min"],
functions=["mean", "max"],
freq="30min",
)
X_tr = win_f.fit_transform(X['ambient_temp'].to_frame())
X_tr.head()
ambient_temp ambient_temp_window_45min_mean \
2020-05-15 12:00:00 31.31 NaN
2020-05-15 12:15:00 31.51 NaN
2020-05-15 12:30:00 32.15 31.310000
2020-05-15 12:45:00 32.39 31.410000
2020-05-15 13:00:00 32.62 31.656667
ambient_temp_window_45min_max
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 31.31
2020-05-15 12:45:00 31.51
2020-05-15 13:00:00 32.15
And if we do not want the original values of time series in the returned dataframe, we just need to remember to drop the original series after the transformation:
win_f = WindowFeatures(
window=["45min"],
functions=["mean", "max"],
freq="30min",
drop_original=True,
)
X_tr = win_f.fit_transform(X['ambient_temp'].to_frame())
X_tr.head()
ambient_temp_window_45min_mean \
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 31.310000
2020-05-15 12:45:00 31.410000
2020-05-15 13:00:00 31.656667
ambient_temp_window_45min_max
2020-05-15 12:00:00 NaN
2020-05-15 12:15:00 NaN
2020-05-15 12:30:00 31.31
2020-05-15 12:45:00 31.51
2020-05-15 13:00:00 32.15
Getting the name of the variables#
We can easily obtain the name of the original and new variables with the method
get_feature_names_out. By using the method with the default parameters, we obtain
all the features in the output dataframe.
win_f = WindowFeatures()
win_f.fit(X)
win_f.get_feature_names_out()
['ambient_temp',
'module_temp',
'irradiation',
'color',
'ambient_temp_window_3_mean',
'module_temp_window_3_mean',
'irradiation_window_3_mean']
Alternatively, we can obtain the names of the lag features created from one or more input features as follows:
win_f.get_feature_names_out(input_features=["irradiation"])
['irradiation_window_3_mean']