Statistics
The statistics
integration observes the state of a source sensor and provides aggregated statistical characteristics about its recent past. This integration can be useful in automation, for example, to trigger an action when the air humidity in the bathroom settles after a hot shower or when the number of brewed coffees over a day gets too high.
The statistics sensor updates with every update of the source sensor, for which the numeric sensor
and binary_sensor
are supported. The time period and/or number of recent state changes, which should be considered, must be given in configuration. Check the configuration section below for details.
Assuming the recorder
integration is running, 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 some time for the sensor to start reporting data because some characteristics calculations require more than one source sensor value.
The statistics
integration is different to Longterm Statistics. More details on the differences can be found in the 2021.8.0 release notes.
Configuration
To add the Statistics helper to your Home Assistant instance, use this My button:
Manual configuration steps
If the above My button doesn’t work, you can also perform the following steps manually:

Browse to your Home Assistant instance.

At the top of the screen, select the tab: Helpers.

In the bottom right corner, select the
Create helper button. 
From the list, select Statistics.

Follow the instructions on screen to complete the setup.
Further information about these configuration options can be found under the YAML configuration
Defines whether the most recent sampled value should be preserved regardless of the “Max age” setting.
Only relevant in combination with the percentile characteristic. Must be a value between 1 and 99.
Characteristics
The following options for the configuration parameter state_characteristic
are available as staticical characteristics. Pay close attention to the correct configuration of sampling_size
and/or max_age
, as most characteristics are directly influenced by these settings.
Numeric Source Sensor
The following are supported for sensor
source sensors state_characteristic
:
State Characteristic  Description 

average_linear 
The average value of stored measurements under consideration of the time distances between them. A linear interpolation is applied per measurement pair. Suited to observe a source sensor with frequent, nonperiodic sensor updates and when continuous behavior is represented by the measurements (e.g. your electricity consumption). WARNING: This type of average may show unexpected behavior in cases where values remain stable (e.g. an equally spaced sequence of t1:0 > t2:0 > t3:0 > t4:0 > t5:10 would produce an average of 5 instead of 1,25 because the nonchanging zeros in the middle (t2 to t4) are being filtered out by Home Assistant and so the actually computed curve already starts rising at t1 instead of staying flat until t4). Accordingly, this function is only making sense in the context of noisy sensors that keep changing constantly. 
average_step 
The average value of stored measurements under consideration of the time distances between them. LOCF (last observation carried forward weighting) is applied, meaning, that the old value is assumed between two measurements. This is a better fit to how Home Assistant deals with constant values (compared to the linear function) and it is also better fitting to sensors that are switching between stable phases (e.g. a heating level set to either 1, 2 or 3). WARNING: Even when a time interval is specified by the max age parameter, the average is not necessarily matching the average over that whole interval (e.g. when values were removed due to the sampling size limit). 
average_timeless 
The average value of stored measurements. This method assumes that all measurements are equally spaced and, therefore, time is ignored and a simple average of values is computed. Equal to mean . 
change_sample 
The average change per sample. The difference between the newest and the oldest measurement is divided by the number of inbetween measurements (n1). 
change_second 
The average change per second. The difference between the newest and the oldest measurement is divided by seconds between them. 
change 
The difference between the newest and the oldest measurement. 
count 
The number of stored source sensor readings. This number is limited by sampling_size and can be low within the bounds of max_age . 
datetime_newest 
The timestamp of the newest measurement. 
datetime_oldest 
The timestamp of the oldest measurement. 
datetime_value_max 
The timestamp of the numerically biggest measurement. 
datetime_value_min 
The timestamp of the numerically smallest measurement. 
distance_95_percent_of_values 
A statistical indicator derived from the standard deviation of an assumed normal distribution. 95% of all stored values fall into a range of returned size. 
distance_99_percent_of_values 
A statistical indicator derived from the standard deviation of an assumed normal distribution. 99% of all stored values fall into a range of returned size. 
distance_absolute 
The difference or “spread” between the extreme values of measurements. Equals value_max minus value_min . 
mean 
The average value computed for all measurements. Be aware that this does not consider uneven time intervals between measurements. 
mean_circular 
The circular mean 
median 
The median 
noisiness 
A simplified version of a signaltonoise ratio. A high value indicates a quickly changing source sensor value, a small value will be seen for a steady source sensor. The absolute change between subsequent source sensor measurement values is summed up and divided by the number of intervals. 
percentile 
Percentilespercentile is needed, see below. 
standard_deviation 
The standard deviation 
sum 
The mathematical sum of all source sensor measurement values within the given time and sampling size limits. 
sum_differences 
The mathematical sum of differences between subsequent source sensor measurement values within the given time and sampling size limits. 
sum_differences_nonnegative 
The mathematical sum of nonnegative differences between subsequent source sensor measurement values within the given time and sampling size limits. The characteristic assumes that the source sensor value can only increase, but might occasionally be reset to zero. If a value is smaller than the previous value, the function assumes the previous value should have been a zero. 
total 
The mathematical sum of all source sensor measurement values within the given time and sampling size limits. Equal to sum . 
value_max 
The largest value among the number of measurements. 
value_min 
The smallest value among the number of measurements. 
variance 
The variance 
Binary Source Sensor
The following are supported for binary_sensor
source sensors state_characteristic
:
State Characteristic  Description 

average_step 
A percentage of time across all stored measurements, in which the binary source sensor was “On”. If over the course of one hour, movement was detected for 6 minutes, the average_step is 10%. 
average_timeless 
The percentage of stored measurements, for which the binary source sensor was “On”. Time in on/off states is ignored. If over the course of one hour, a single movement was detected, the average_timeless is 33.3% (assuming the stored measurements “Off”, “On”, “Off”). Equal to mean . 
count 
The number of stored source sensor readings. 
count_on 
The number of stored source sensor readings with the value “On”. Be aware that only value changes are registered by default, except if the source sensor has the property force_update set to true. 
count_off 
The number of stored source sensor readings with the value “Off”. Be aware that only value changes are registered by default, except if the source sensor has the property force_update set to true. 
datetime_newest 
The timestamp of the newest measurement. 
datetime_oldest 
The timestamp of the oldest measurement. 
mean 
The percentage of stored measurements, for which the binary source sensor was “On”. Time in on/off states is ignored. If over the course of one hour, a single movement was detected, the average_timeless is 33.3% (assuming the stored measurements “Off”, “On”, “Off”). 
Attributes
A statistics sensor presents the following attributes for context about its internal status.
Attribute  Description 

age_coverage_ratio 
Only when max_age is defined. Ratio (0.01.0) of the configured age of source sensor measurements considered (time period max_age ) covered inbetween the oldest and newest stored values. A low number can indicate an unwanted mismatch between the configured limits and the source sensor behavior. The value 1.0 represents at least two values covering the full time period. Value 0 is the result of only one measurement considered. The sensor turns Unknown if no measurements are stored. 
buffer_usage_ratio 
Only when sampling_size is defined. Ratio (0.01.0) of the configured buffer size used by the stored source sensor measurements. A low number can indicate an unwanted mismatch between the configured limits and the source sensor behavior. The value 1.0 represents a full buffer, value 0 stands for an empty one. 
source_value_valid 
True/false indication whether the source sensor supplies valid values to the statistics sensor (judged by the last value received). 
YAML configuration
Define a statistics sensor by adding lines similar to the following examples to your configuration.yaml
:
sensor:
 platform: statistics
name: "Bathroom humidity mean over last 24 hours"
entity_id: sensor.bathroom_humidity
state_characteristic: mean
max_age:
hours: 24
 platform: statistics
entity_id: binary_sensor.movement
state_characteristic: count_on
sampling_size: 100
 platform: statistics
name: "Bathroom humidity change over 5 minutes"
entity_id: sensor.bathroom_humidity
state_characteristic: change
max_age:
minutes: 5
sampling_size: 50
precision: 1
Configuration Variables
The source sensor to observe and compute statistical characteristics for. Only sensors and binary sensors are supported.
The characteristic that should be used as the state of the statistics sensor (see tables above).
Maximum number of source sensor measurements stored. Be sure to choose a reasonably high number or omit if the samples should be driven by max_age
instead. A statistics sensor requires sampling_size
, max_age
, or both to be defined.
Maximum age of source sensor measurements stored. Setting this to a time period will cause older values to be discarded. If omitted, the number of considered source sensor measurements is limited by sampling_size
only. Set both parameters appropriately to create suited limits for your use case. The sensor value will become unknown
if the source sensor is not updated within the time period. A statistics sensor requires sampling_size
, max_age
, or both to be defined.
Defines whether the most recent sampled value should be preserved regardless of the max_age
setting.
Only relevant in combination with the percentile
characteristic. Must be a value between 1 and 99. The value defines the percentile value to consider. The 25th percentile is also known as the first quartile, the 50th percentile as the median.
Defines the number of decimal places of the calculated sensor value.
An important note on max_age and sampling_size
If both max_age
and sampling_size
are given, the considered samples are those within the max_age
time window but limited to the number of sampling_size
newest samples. Specify a wideenough sampling_size
if using an extended max_age
(e.g., when looking for max_age
1 hour, a sensor that produces one measurement per minute should have at least a sampling_size
of 60 to use all samples within the max_age
timeframe.)
If only sampling_size
is given there is no time limit. If only max_age
is given the considered number of samples is unlimited.