All functions listed here are supported in:
Some general notes on function parameters:
<
>
/host/key
and (sec|#num)<:time shift>
parameters must never be quoted/host/key
is a common mandatory first parameter for the functions referencing the host item history(sec|#num)<:time shift>
is a common second parameter for the functions referencing the host item history, where:
Some general notes on function parameters:
<
>
/host/key
and (sec|#num)<:time shift>
parameters must never be quotedThe future value, max, min, delta or avg of the item.
Supported value types: Float, Integer.
Parameters:
now
+ time
; max, min, delta and avg investigate the item value estimate on the interval between now
and now
+ time
.Comments:
Examples:
forecast(/host/key,#10,1h) #forecast the item value in one hour based on the last 10 values
forecast(/host/key,1h,30m) #forecast the item value in 30 minutes based on the last hour data
forecast(/host/key,1h:now-1d,12h) #forecast the item value in 12 hours based on one hour one day ago
forecast(/host/key,1h,10m,"exponential") #forecast the item value in 10 minutes based on the last hour data and exponential function
forecast(/host/key,1h,2h,"polynomial3","max") #forecast the maximum value the item can reach in the next two hours based on last hour data and cubic (third degree) polynomial
forecast(/host/key,#2,-20m) #estimate the item value 20 minutes ago based on the last two values (this can be more precise than using last(), especially if the item is updated rarely, say, once an hour)
The time in seconds needed for an item to reach the specified threshold.
Supported value types: Float, Integer.
Parameters:
Comments:
Examples:
timeleft(/host/key,#10,0) #the time until the item value reaches zero based on the last 10 values
timeleft(/host/key,1h,100) #the time until the item value reaches 100 based on the last hour data
timeleft(/host/key,1h:now-1d,100) #the time until the item value reaches 100 based on one hour one day ago
timeleft(/host/key,1h,200,"polynomial2") #the time until the item value reaches 200 based on the last hour data and assumption that the item behaves like a quadratic (second degree) polynomial