Filter
The filter
platform enables sensors that process the states of other entities.
filter
applies a signal processing algorithm to a sensor, previous and current states, and generates a new state
given the chosen algorithm. The next image depicts an original sensor and the filter sensor of that same sensor using the History Graph component.
Configuration
To enable Filter Sensors in your installation, add the following to your configuration.yaml
file:
# Example configuration.yaml entry
sensor:
 platform: filter
name: "filtered realistic humidity"
entity_id: sensor.realistic_humidity
filters:
 filter: outlier
window_size: 4
radius: 4.0
 filter: lowpass
time_constant: 10
precision: 2
 platform: filter
name: "filtered realistic temperature"
entity_id: sensor.realistic_temperature
filters:
 filter: outlier
window_size: 4
radius: 2.0
 filter: lowpass
time_constant: 10
 filter: time_simple_moving_average
window_size: "00:05"
precision: 2
Filters can be chained and are applied according to the order present in the configuration file.
Configuration Variables
 entity_id
(string)(Required)
The entity ID of the sensor to be filtered.
 name
(string)(Optional)
Name to use in the frontend.
 filters
(list)(Required)
Filters to be used.
 filter
(string)(Required)
Algorithm to be used to filter data. Available filters are
lowpass
,outlier
,range
,throttle
,time_throttle
andtime_simple_moving_average
. window_size

(integer  time)(Optional)
Size of the window of previous states. Time based filters such as
time_simple_moving_average
will require a time period (size in time), while other filters such asoutlier
will require an integer (size in number of states). Time periods are in hh:mm format and must be quoted.Default value:
1
 precision

(integer)(Optional)
See lowpass filter. Defines the precision of the filtered state, through the argument of round().
Default value:
None
 time_constant

(integer)(Optional)
See lowpass filter. Loosely relates to the amount of time it takes for a state to influence the output.
Default value:
10
 radius

(float)(Optional)
See outlier filter. Band radius from median of previous states.
Default value:
2.0
 type

(string)(Optional)
See time_simple_moving_average filter. Defines the type of Simple Moving Average.
Default value:
last
 lower_bound

(float)(Optional)
See range filter. Lower bound for filter range.
Default value:
negative infinity
 upper_bound

(float)(Optional)
See range filter. Upper bound for filter range.
Default value:
positive infinity
Filters
Lowpass
The Lowpass filter (lowpass
) is one of signal processing most common filters, as it smooths data by shortcutting peaks and valleys.
The included Lowpass filter is very basic and is based on exponential smoothing, in which the previous data point is weighted with the new data point.
B = 1.0 / time_constant
A = 1.0  B
LowPass(state) = A * previous_state + B * state
The returned value is rounded to the number of decimals defined in (precision
).
Outlier
The Outlier filter (outlier
) is a basic Bandpass filter, as it cuts out any value outside a specific range.
The included Outlier filter will discard any value beyond a band centered on the median of the previous values, replacing it with the median value of the previous values. If inside the band, the
distance = abs(state  median(previous_states))
if distance > radius:
median(previous_states)
else:
state
Throttle
The Throttle filter (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 10%, the window_size
should be set to 10, for 50% should be set to 2.
This filter is relevant when you have a sensor which produces states at a very highrate, which you might want to throttle down for storing or visualization purposes.
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.
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 by Andreas Eckner.
The paper defines three types/versions of the Simple Moving Average (SMA): last, next and linear. Currently only last is implemented.
Theta, as described in the paper, is the window_size
parameter, and can be expressed using time notation (e.g., “00:05” for a five minutes time window).
Range
The Range filter (range
) restricts incoming data to a range specified by a lower and upper bound.
All values greater then the upper bound are replaced by the upper bound and all values lower than the lower bound are replaced by the lower bound. Per default there are neither upper nor lower bound.
if new_state > upper_bound:
upper_bound
if new_state < lower_bound:
lower_bound
new_state