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Exponential Smoothing Implementation in Splunk

Path Finder


I am planning to implement exponential smoothing in Splunk based on below formula where
s1 is the forecasted value. At time t=0, it is equal to first event. For time=t, it is calculated based on below formula. I can hard code value for "alpha".

s{t}=[alpha * x{t-1}] + [(1-alpha)s{t-1}], t>1

For time=t, it is referring to previously calculated forecast value (s{t-1}) and previous event value (x{t-1}) so not sure how this can be achieved using Splunk.
Say the log data is like below and "total" is the field which needs to be used(x{t}) to calcuate forecasted value(s{t}). I know there will be a field named "total" created which contains all the values but is there a way I can refer to say first value in field "total" like total0 which will be equal to 4, total[1] which will be equal to 6?

1/2/13 2:30:00.000 PM total=4
1/2/13 2:31:00.000 PM total=6
1/2/13 2:32:00.000 PM total=8
1/2/13 2:33:00.000 PM total=10

Any help is greatly appreciated.

0 Karma


The trick here is to make all the data required for the calculation in the current event.

Looking at the formula, it only relies on the previous value of x and the previous value of s.

You can pull the previous value of a field into the current event like this :

... | streamstats window=1 current=f total as prev_total

so now you have access to x{t} and x{t-1} in the event. ( total and prev_total fields respectively)

You'll also need to pre-populat the 1st valid value of s, then you can use the above method to 'stream' the previous value of s into the current event to calculate s{t}

Splunk Employee
Splunk Employee

To do a cumulative total in a new field, take a look at the function eventstats.

and maybe too at the function predict that may already do what you want.

0 Karma

Path Finder

Thanks for the reply But my requirement is little different. This formula expects values from previous calculated results so I would like to know if there is a way I can refer to field values separately like arrays as specified in my question above.

s{t}=[alpha * x{t-1}] + [(1-alpha)s{t-1}], t>1

0 Karma
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