statistics sensor platform consumes the state from other sensors. It exports the
mean value as state and the following values as attributes:
change_rate. If the source is a binary sensor then only state changes are counted.
recorder integration is running (either configured explicitly or as part of a meta-integration/dependency, e.g.,
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
# enable the recorder integration (optional) recorder: # Example configuration.yaml entry sensor: - platform: statistics entity_id: sensor.cpu - platform: statistics entity_id: binary_sensor.movement
Size of the sampling. If the limit is reached then the values are rotated.
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.
Defines the precision of the calculated values, through the argument of
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.
Indicates whether quantiles are computed using the
exclusive method (default) or
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).