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8 Forecasting functions


Sometimes there are signs of the upcoming problem. These signs can be spotted so that actions may be taken in advance to prevent or at least minimize the impact of the problem. Zabbix has tools to predict the future behaviour of the monitored system based on historic data.

1 Functions

Two things one needs to know is how to define a problem state and how much time is needed to take action. Then there are two ways to set up a trigger signalling about a potential unwanted situation. First: trigger must fire when system after “time to act” is expected to be in problem state. Second: trigger must fire when system is going to reach the problem state in less than “time to act”. Corresponding trigger functions to use are forecast and timeleft. Note that underlying statistical analysis is basically identical for both functions. You may set up a trigger whichever way you prefer with similar results.

2 Parameters

Both functions use almost the same set of parameters. Use the list of supported functions for reference.

2.1 Time interval

First of all you should specify the historic period Zabbix should analyse to come up with prediction. You do it in a familiar way by means of sec or #num parameter and optional time_shift like you do it with avg, count, delta, max, min and sum functions.

2.2 Forecasting horizon

(forecast only)
Parameter time specifies how far in the future Zabbix should extrapolate dependencies it finds in historic data. No matter if you use time_shift or not, time is always counted starting from the current moment.

2.3 Threshold to reach

(timeleft only)
Parameter threshold specifies a value the analysed item has to reach, no difference if from above or from below. Once we have determined f(t) (see below) we should solve equation f(t) = threshold and return the root which is closer to now and to the right from now or 999999999999.9999 if there is no such root.

2.4 Fit functions

Default fit is the linear function. But if your monitored system is more complicated you have more options to choose from.

fit x = f(t)
linear x = a + b*t
polynomialN1) x = a0 + a1*t + a2*t2 + … + an*tn
exponential x = a*exp(b*t)
logarithmic x = a + b*log(t)
power x = a*tb
2.5 Modes

(forecast only)
Every time a trigger function is evaluated it gets data from the specified history period and fits a specified function to the data. So, if the data is slightly different the fitted function will be slightly different. If we simply calculate the value of the fitted function at a specified time in the future you will know nothing about how the analysed item is expected to behave between now and that moment in the future. For some fit options (like polynomial) a simple value from the future may be misleading.

mode forecast result
value f(now + time)
max maxnow <= t <= now + time f(t)
min minnow <= t <= now + time f(t)
delta max - min
avg average of f(t) (now <= t <= now + time) according to definition

3 Details

To avoid calculations with huge numbers we consider the timestamp of the first value in specified period plus 1 ns as a new zero-time (current epoch time is of order 109, epoch squared is 1018, double precision is about 10-16). 1 ns is added to provide all positive time values for logarithmic and power fits which involve calculating log(t). Time shift does not affect linear, polynomial, exponential (apart from easier and more precise calculations) but changes the shape of logarithmic and power functions.

4 Potential errors

Functions return -1 in such situations:

  • specified evaluation period contains no data;
  • result of mathematical operation is not defined2);
  • numerical complications (unfortunately, for some sets of input data range and precision of double-precision floating-point format become insufficient)3).

Note: No warnings or errors are flagged if chosen fit poorly describes provided data or there is just too few data for accurate prediction.

See also

Polynomial degree can be from 1 to 6, polynomial1 is equivalent to linear. However, use higher degree polynomials with caution (More info here). If the evaluation period contains less points than needed to determine polynomial coefficients, polynomial degree will be lowered (e.g polynomial5 is requested, but there are only 4 points, therefore polynomial3 will be fitted).
For example fitting exponential or power functions involves calculating log() of item values. If data contains zeros or negative numbers you will get an error since log() is defined for positive values only.
For linear, exponential, logarithmic and power fits all necessary calculations can be written explicitly. For polynomial only value can be calculated without any additional steps. Calculating avg involves computing polynomial antiderivative (analytically). Computing max, min and delta involves computing polynomial derivative (analytically) and finding its roots (numerically). Solving f(t) = 0 involves finding polynomial roots (numerically).