Add root mean square feature

tests/units/feature_extraction/test_feature_calculations.py

@@ -463,6 +463,13 @@ def test_kurtosis(self):
         self.assertAlmostEqualOnAllArrayTypes(kurtosis, [1, 1, 1, 1], 0)
         self.assertIsNanOnAllArrayTypes(kurtosis, [1, 1, 1])
 
+    def test_root_mean_square(self):
+        self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [1, 1, 1, 2, 2], 1.48324)
+        self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [0], 0)
+        self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [], 0)
+        self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [1], 1)
+        self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [-1], 1)
+
     def test_absolute_sum_of_changes(self):
         self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, 1, 1, 1, 2, 1], 2)
         self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, -1, 1, -1], 6)

tsfresh/feature_extraction/feature_calculators.py

@@ -741,6 +741,20 @@ def kurtosis(x):
     return pd.Series.kurtosis(x)
 
 
+@set_property("fctype", "simple")
+@set_property("minimal", True)
+def root_mean_square(x):
+    """
+    Returns the root mean square (rms) of the time series.
+
+    :param x: the time series to calculate the feature of
+    :type x: numpy.ndarray
+    :return: the value of this feature
+    :return type: float
+    """
+    return np.sqrt(np.mean(x**2))
+
+
 @set_property("fctype", "simple")
 def absolute_sum_of_changes(x):
     """