7 Prediction functions

All functions listed here are supported in:

Some general notes on function parameters:

Function parameters are separated by a comma
Optional function parameters (or parameter parts) are indicated by < >
Function-specific parameters are described with each function
/host/key and (sec|#num)<:time shift> parameters must never be quoted

Common parameters

/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:
- sec - maximum evaluation period in seconds (time suffixes can be used), or
- #num - maximum evaluation range in latest collected values (if preceded by a hash mark)
- time shift (optional) allows to move the evaluation point back in time. See more details on specifying time shift.

Prediction functions

FUNCTION
	Description	Function-specific parameters	Comments
forecast (/host/key,(sec\|#num)<:time shift>,time,<fit>,<mode>)
	Future value, max, min, delta or avg of the item.	See common parameters. time - forecasting horizon in seconds (time suffixes can be used); negative values are supported fit (optional; must be double-quoted) - function used to fit historical data Supported `fits`: linear - linear function polynomialN - polynomial of degree N (1 <= N <= 6) exponential - exponential function logarithmic - logarithmic function power - power function Note that: linear is default, polynomial1 is equivalent to linear mode (optional; must be double-quoted) - demanded output Supported `modes`: value - value (default) max - maximum min - minimum delta - max-min avg - average Note that: value estimates item value at the moment `now` + `time` max, min, delta and avg investigate item value estimate on the interval between `now` and `now` + `time`	Supported value types: float, int If value to return is larger than 1.7976931348623157E+308 or less than -1.7976931348623157E+308, return value is cropped to 1.7976931348623157E+308 or -1.7976931348623157E+308 correspondingly. Becomes unsupported only if misused in expression (wrong item type, invalid parameters), otherwise returns -1 in case of errors. Examples: => forecast(/host/key,#10,1h) → forecast item value in one hour based on the last 10 values => forecast(/host/key,1h,30m) → forecast item value in 30 minutes based on the last hour data => forecast(/host/key,1h:now-1d,12h) → forecast item value in 12 hours based on one hour one day ago => forecast(/host/key,1h,10m,”exponential”) → forecast 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 item is updated rarely, say, once an hour) See also additional information on predictive trigger functions.
timeleft (/host/key,(sec\|#num)<:time shift>,threshold,<fit>)
	Time in seconds needed for an item to reach a specified threshold.	See common parameters. threshold - value to reach (unit suffixes can be used) fit (optional; must be double-quoted) - see forecast()	Supported value types: float, int If value to return is larger than 1.7976931348623157E+308, return value is cropped to 1.7976931348623157E+308. Returns 1.7976931348623157E+308 if threshold cannot be reached. Becomes unsupported only if misused in the expression (wrong item type, invalid parameters), otherwise returns -1 in case of errors. Examples: => timeleft(/host/key,#10,0) → time until the item value reaches zero based on the last 10 values => timeleft(/host/key,1h,100) → time until the item value reaches 100 based on the last hour data => timeleft(/host/key,1h:now-1d,100) → time until the item value reaches 100 based on one hour one day ago => timeleft(/host/key,1h,200,”polynomial2”) → time until the item value reaches 200 based on the last hour data and assumption that the item behaves like quadratic (second degree) polynomial See also additional information on predictive trigger functions.