The statistics sensor platform consumes the state from other sensors. It exports the mean value as state and the following values as attributes: count, mean, median, quantiles, standard_deviation, variance, total, min_value, max_value, min_age, max_age, change, average_change and change_rate. If the source is a binary sensor then only state changes are counted.

Assuming the recorder integration is running (either configured explicitly or as part of a meta-integration/dependency, e.g., default_config, history, etc.), historical sensor data is read from the database on startup and is available immediately after a restart of the platform. If the recorder integration is not running, it can take time for the sensor to start reporting data because some attribute calculations require more than one value.


To enable the statistics sensor, add the following lines to your configuration.yaml:

# enable the recorder integration (optional)

# Example configuration.yaml entry
  - platform: statistics
    entity_id: sensor.cpu
  - platform: statistics
    entity_id: binary_sensor.movement

Configuration Variables

entity_id string Required

The entity to monitor. Only sensors and binary sensor.

name string (Optional, default: Stats)

Name of the sensor to use in the frontend.

sampling_size integer (Optional, default: 20)

Size of the sampling. If the limit is reached then the values are rotated.

max_age time (Optional)

Maximum age of measurements. Setting this to a time interval will cause older values to be discarded. Please note that you might have to increase the sampling_size parameter. If you e.g., have a sensor value updated every second you will by default only get a max_age of 20s. Furthermore the sensor gets unknown if the entity is not updated within the time interval.

precision integer (Optional, default: 2)

Defines the precision of the calculated values, through the argument of round().

quantile_intervals integer (Optional, default: 4)

Number of continuous intervals with equal probability. Value must be an integer higher than 1. In addition, quantiles will be unknown unless the number of quantile intervals is lower than the number of data points (count). Set it to 4 for quartiles (default) or to 100 for percentiles, for example.

quantile_method string (Optional, default: exclusive)

Indicates whether quantiles are computed using the exclusive method (default) or inclusive. The exclusive method assumes the population data have more extreme values than the sample, and therefore, the part under the i-th of m sorted data points is computed as i / (m + 1). The inclusive method assumes that the sample data includes the more extreme values from the population, and therefore, the part under the i-th of m sorted data points is computed as (i - 1) / (m - 1).