Temporal functions
You can use temporal functions to return values based on timestamps of data points.
Using temporal functions in metrics
In metrics only, you can use the following functions that return values based on timestamps of data points.
Temporal function arguments must be properties from the local asset model or nested expressions. This means that you can't use properties from child asset models in temporal functions.
You can use nested expressions in temporal functions. When you use nested expressions, the following rules apply:

Each argument can have only one variable.
For example,
latest( t*9/5 + 32 )
is supported. 
Arguments can't be aggregation functions.
For example,
first( sum(x) )
isn't supported.
Function  Description 


Returns the given variable's value with the earliest timestamp over the current time interval. 

Returns the given variable's value with the latest timestamp over the current time interval. 

Returns the given variable's value with the earliest timestamp before the current time interval. This function computes a data point for every time interval, if the input property has at least one data point in its history. 

Returns the given variable's value with the latest timestamp before the end of the current time interval. This function computes a data point for every time interval, if the input property has at least one data point in its history. 

Returns the amount of time in seconds that the given variables are
positive over the current time interval. You can use the comparison functions to
create a transform property for the For example, if you have an This function doesn't support metric properties as input variables. This function computes a data point for every time interval, if the input property has at least one data point in its history. 

Returns the average of input data weighted with time intervals between points. See Time weighted functions parameters for computation and intervals details.The optional argument


Returns the standard deviation of input data weighted with time intervals between points. See Time weighted functions parameters for computation and intervals details. The calculation uses the Last Observed Carry Forward computation algorithm for intervals between data points. In this approach, the data point is computed as the last observed value until the next input data point time stamp. Weight is computed as time interval in seconds between data points or window boundaries. The optional argument
The following formulas are used for computation where:
Equation for population standard deviation: Equation for frequency standard deviation: 
The following diagram shows how AWS IoT SiteWise computes the temporal functions
first
, last
, earliest
, and
latest
, relative to the current time interval.
Timeweighted functions parameters
Timeweighted functions computed for the aggregate window take into account the following:

Data points inside the window

Time intervals between data points

Last data point before the window

First data point after the window (for some algorithms)
Terms:

Bad data point – Any data point with nongood quality or nonnumber value. This is not considered in a window result computation.

Bad interval – The interval after a bad data point. The interval before the first known data point is also considered a bad interval.

Good data point – Any data point with good quality and numeric value.

AWS IoT SiteWise only consumes
GOOD
quality data when it computes transforms and metrics. It ignoresUNCERTAIN
andBAD
data points. 
The interval before the first known data point is considered a bad interval. See Formula expression tutorials for more information.
The interval after the last known data point continues indefinitely, affecting all following windows. When a new data point arrives, the function recomputes the interval.
Following the rules above, the aggregate window result is computed and limited to window boundaries. By default, the function only sends the window result if the whole window is a good interval.
If the window good interval is smaller than the window length, the function does not send the window.
When the data points affecting the window result change, the function recalculates the window, even if the data points are outside of the window.
If the input property has at least one data point in its history and a computation has been initiated, the function calculates the timeweighted aggregate functions for every time interval.
Example statetime scenario
Consider an example where you have an asset with the following properties:

Idle
– A measurement that is0
or1
. When the value is1
, the machine is idle. 
Idle Time
– A metric that uses the formulastatetime(Idle)
to calculate the amount of time in seconds where the machine is idle, per 1 minute interval.
The Idle
property has the following data points.
Timestamp  2:00:00 PM  2:00:30 PM  2:01:15 PM  2:02:45 PM  2:04:00 PM 
Idle  0  1  1  0  0 
AWS IoT SiteWise calculates the Idle Time
property every minute from the
values of Idle
. After this calculation completes, the Idle
Time
property has the following data points.
Timestamp  2:00:00 PM  2:01:00 PM  2:02:00 PM  2:03:00 PM  2:04:00 PM 
Idle Time  N/A  30  60  45  0 
AWS IoT SiteWise performs the following calculations for Idle Time
at the
end of each minute.

At 2:00 PM (for 1:59 PM to 2:00 PM)

There is no data for
Idle
before 2:00 PM, so no data point is calculated.


At 2:01 PM (for 2:00 PM to 2:01 PM)

At 2:00:00 PM, the machine is active (
Idle
is0
). 
At 2:00:30 PM, the machine is idle (
Idle
is1
). 
Idle
doesn't change again before the end of the interval at 2:01:00 PM, soIdle Time
is 30 seconds.


At 2:02 PM (for 2:01 PM to 2:02 PM)

At 2:01:00 PM, the machine is idle (per the last data point at 2:00:30 PM).

At 2:01:15 PM, the machine is still idle.

Idle
doesn't change again before the end of the interval at 2:02:00 PM, soIdle Time
is 60 seconds.


At 2:03 PM (for 2:02 PM to 2:03 PM)

At 2:02:00 PM, the machine is idle (per the last data point at 2:01:15 PM).

At 2:02:45 PM, the machine is active.

Idle
doesn't change again before the end of the interval at 2:03:00 PM, soIdle Time
is 45 seconds.


At 2:04 PM (for 2:03 PM to 2:04 PM)

At 2:03:00 PM, the machine is active (per the last data point at 2:02:45 PM).

Idle
doesn't change again before the end of the interval at 2:04:00 PM, soIdle Time
is 0 seconds.

Example TimeWeightedAvg and TimeWeightedStDev scenario
The following tables provide sample inputs and outputs for these oneminute
window metrics: Avg(x), TimeWeightedAvg(x), TimeWeightedAvg(x, "linear"),
stDev(x), timeWeightedStDev(x), timeWeightedStDev(x, 'p')
.
Sample input for oneminute aggregate window:
These data points all have GOOD quality.
03:00:00  4.0 
03:01:00  2.0 
03:01:10  8.0 
03:01:50  20.0 
03:02:00  14.0 
03:02:05  10.0 
03:02:10  3.0 
03:02:30  20.0 
03:03:30  0.0 
Aggregate results output:
None – Result not produced for this window.
Time  Avg(x) 
TimeWeightedAvg(x) 
TimeWeightedAvg(X, "linear") 
stDev(X) 
timeWeightedStDev(x) 
timeWeightedStDev(x, 'p') 

3:00:00  4  None  None  0  None  None 
3:01:00  2  4  3  0  0  0 
3:02:00  14  9  13  6  5.430610041581775  5.385164807134504 
3:03:00  11  13  12.875  8.54400374531753  7.724054437220943  7.659416862050705 
3:04:00  0  10  2.5  0  10.084389681792215  10 
3:05:00  None  0  0  None  0  0 
Using temporal functions in transforms
In transforms only, you can use the
pretrigger()
function to retrieve the GOOD
quality value
for a variable prior to the property update that initiated the current transform
calculation.
Consider an example where a manufacturer uses AWS IoT SiteWise to monitor the status of a machine. The manufacturer uses the following measurements and transforms to represent the process:

A measurement,
current_state
, that can be 0 or 1.
If the machine is in the cleaning state,
current_state
equals 1. 
If the machine is in the manufacturing state,
current_state
equals 0.


A transform,
cleaning_state_duration
, that equalsif(pretrigger(current_state) == 1, timestamp(current_state)  timestamp(pretrigger(current_state)), none)
. This transform returns how long the machine has been in the cleaning state in seconds, in the Unix epoch format. For more information, see Conditional functions and the timestamp() function.
If the machine stays in the cleaning state longer than expected, the manufacturer might investigate the machine.
You can also use the pretrigger()
function in multivariate
transforms. For example, you have two measurements named x
and
y
, and a transform, z
, that equals x + y +
pretrigger(y)
. The following table shows the values for x
,
y
, and z
from 9:00 AM to 9:15 AM.

This example assumes that the values for the measurements arrive chronologically. For example, the value of
x
for 09:00 AM arrives before the value ofx
for 09:05 AM. 
If the data points for 9:05 AM arrive before the data points for 9:00 AM,
z
isn't calculated at 9:05 AM. 
If the value of
x
for 9:05 AM arrives before the value ofx
for 09:00 AM and the values ofy
arrive chronologically,z
equals22 = 20 + 1 + 1
at 9:05 AM.
09:00 AM  09:05 AM  09:10 AM  09:15 AM  


10 
20 
30 


1 
2 
3 



23 = 20 + 2 + 1

25 = 20 + 3 + 2

36 = 30 + 3 + 3
