---
layout: page
title: "Filter Sensor"
description: "Instructions on how to integrate Data Filter Sensors into Home Assistant."
date: 2018-02-20
sidebar: true
comments: false
sharing: true
footer: true
ha_category: Sensor
ha_release: 0.65
ha_iot_class: "Local Push"
logo: home-assistant.png
---

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]({{site_roor}}/components/history_graph/) component.

<p class='img'>
  <img src='{{site_root}}/images/screenshots/filter-sensor.png' />
</p>

To enable Filter Sensors in your installation, add the following to your `configuration.yaml` file:


```yaml
# 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 %}
entity_id:
  description: The entity ID of the sensor to be filtered.
  required: true
  type: string
name:
  description: Name to use in the frontend.
  required: false
  type: string
filters:
  description: Filters to be used.
  required: true 
  type: list
  keys:
    filter:
      description: Algorithm to be used to filter data. Available filters are `lowpass`, `outlier`, `throttle` and `time_simple_moving_average`.
      required: true
      type: string
    window_size:
      description: 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 as `outlier` will require an integer (size in number of states)
      required: false
      type: [int, time]
      default: 1 
    precision:
      description: See [_lowpass_](#low-pass) filter. Defines the precision of the filtered state, through the argument of round().
      required: false
      type: int
      default: None
    time_constant: 
      description: See [_lowpass_](#low-pass) filter. Loosely relates to the amount of time it takes for a state to influence the output.
      required: false
      type: int
      default: 10
    radius: 
      description: See [_outlier_](#outlier) filter. Band radius from median of previous states.
      required: false
      type: float
      default: 2.0
    type:
      description: See [_time_simple_moving_average_](#time-simple-moving-average) filter. Defines the type of Simple Moving Average.
      required: false
      type: string
      default: last
{% endconfiguration %}

## {% linkable_title Filters %}

### {% linkable_title Low-pass %}

The Low-pass filter (`lowpass`) is one of signal processing most common filters, as it smooths data by shortcutting peaks and valleys.

The included Low-pass filter is very basic and is based on [exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing), in which the previous data point is weighted with the new data point.

```python
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`).

### {% linkable_title Outlier %}

The Outlier filter (`outlier`) is a basic Band-pass 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 

```python
distance = abs(state - median(previous_states))

if distance > radius:
    median(previous_states)
else:
    state
```

### {% linkable_title 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 high-rate, which you might want to throttle down 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. 

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).
