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Added Time SMA (#4926)

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* Added Time SMA

* bump version

* update

* ✏️ Minor tweaks

* 🚑 Fixes/Restores ha_release tag

* 🚑 Corrected type
This commit is contained in:
Diogo Gomes 2018-03-18 17:54:38 +00:00 committed by Franck Nijhof
parent 084d69cd98
commit df4739885a
1 changed files with 23 additions and 8 deletions
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31
source/_components/sensor.filter.markdown
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@ -15,7 +15,7 @@ logo: home-assistant.png
The `filter` platform enables sensors that process the states of other entities. 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. `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'> <p class='img'>
<img src='{{site_root}}/images/screenshots/filter-sensor.png' /> <img src='{{site_root}}/images/screenshots/filter-sensor.png' />
@ -37,6 +37,9 @@ sensor:
- filter: lowpass - filter: lowpass
time_constant: 10 time_constant: 10
precision: 2 precision: 2
- 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. Filters can be chained and are applied according to the order present in the configuration file.
@ -53,17 +56,17 @@ name:
filters: filters:
description: Filters to be used. description: Filters to be used.
required: true required: true
type: map type: list
keys: keys:
filter: filter:
description: Algorithm to be used to filter data. Available filters are `lowpass`, `outlier` and `throttle`. description: Algorithm to be used to filter data. Available filters are `lowpass`, `outlier`, `throttle` and `time_simple_moving_average`.
required: true required: true
type: string type: string
window_size: window_size:
description: Size of the window of previous states. 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 required: false
type: int type: [int, time]
default: 5 default: 1
precision: precision:
description: See [_lowpass_](#low-pass) filter. Defines the precision of the filtered state, through the argument of round(). description: See [_lowpass_](#low-pass) filter. Defines the precision of the filtered state, through the argument of round().
required: false required: false
@ -78,14 +81,19 @@ filters:
description: See [_outlier_](#outlier) filter. Band radius from median of previous states. description: See [_outlier_](#outlier) filter. Band radius from median of previous states.
required: false required: false
type: float type: float
default: 2.0 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 %} {% endconfiguration %}
## {% linkable_title Filters %} ## {% linkable_title Filters %}
### {% linkable_title Low-pass %} ### {% linkable_title Low-pass %}
The Low-pass filter (`lowpass`) is one of signal processing most common filters, as it smooths data by shortcuting peaks and valleys. 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 a moving average, in which the previous data point is weighted with the new data point. The included Low-pass filter is very basic and is based on a moving average, in which the previous data point is weighted with the new data point.
@ -120,3 +128,10 @@ 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. 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).