Hi,
this depends on how exactly you plan to calculate the average. The
original definition is based on exponentially decreasing weight of more
distant (older if time is on the x-axis) data points. This (technically)
means that this average at any point X1 depends on all values X0 <= X1.
This would therefore require buffering (using GroupByKey) all elements
in global window, doing the sorting manually and then computing the new
value of the average triggering after each element. This is probably the
technically correct, but most computationally intensive variant.
If the average is done over time intervals, then an other option could
be to define a cut-off interval T, i.e. set the exponentially vanishing
weight of value of data points to be zero at some T0 < T1 - T. If the
data points come at some discrete time-intervals (minutes, hours, days),
then this could mean you can split the data into time sliding windows
(window interval being the cut-off interval, and slide the update
interval) and assign weight for each data point in the particular time
interval - i.e. how much weight does the data point have at the time of
end of the sliding window. With this you could then using CombineFn to
count and sum the weighted averages, which would be much more efficient.
Best,
Jan
On 9/30/23 17:08, Balogh, György wrote:
Hi,
I want to calculate the exponential moving average of a signal using
beam in java.
I understand there is no time order guarantee on incoming data. What
would be the simplest solution for this?
Thank you,
--
György Balogh
CTO
E gyorgy.bal...@ultinous.com <mailto:zsolt.sala...@ultinous.com>
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