diff --git a/source/_components/sensor.filter.markdown b/source/_components/sensor.filter.markdown
index 98f13fb6733..e916082f782 100644
--- a/source/_components/sensor.filter.markdown
+++ b/source/_components/sensor.filter.markdown
@@ -70,7 +70,7 @@ filters:
   type: list
   keys:
     filter:
-      description: Algorithm to be used to filter data. Available filters are  `lowpass`, `outlier`, `range`, `throttle` and `time_simple_moving_average`.
+      description: Algorithm to be used to filter data. Available filters are  `lowpass`, `outlier`, `range`, `throttle`, `time_throttle` and `time_simple_moving_average`.
       required: true
       type: string
     window_size:
@@ -149,6 +149,14 @@ To adjust the rate you need to set the window_size. To throttle a sensor down to
 
 This filter is relevant when you have a sensor which produces states at a very high-rate, which you might want to throttle down for storing or visualization purposes. 
 
+### {% linkable_title Time Throttle %}
+
+The Time Throttle filter (`time_throttle`) will only update the state of the sensor for the first state in the window. This means the filter will skip all other values.
+
+To adjust the rate you need to set the window_size. To throttle a sensor down to 1 value per minute, the `window_size` should be set to 00:01.
+
+This filter is relevant when you have a sensor which produces states at a very high inconstant rate, which you might want to throttle down to some constant rate for storing or visualization purposes. 
+
 ### {% linkable_title Time Simple Moving Average %}
 
 The Time SMA filter (`time_simple_moving_average`) is based on the paper [Algorithms for Unevenly Spaced Time Series: Moving Averages and Other Rolling Operators](http://www.eckner.com/papers/Algorithms%20for%20Unevenly%20Spaced%20Time%20Series.pdf) by Andreas Eckner.