Hi everyone,

I'm forwarding a private conversation to the list with Mats' approval.

The problem is how to compute correlation between time series in Flink. We have two time series, U and V, and need to compute 1000 correlation measures between the series, each measure shifts one series by one more item: corr(U[0:N], V[n:N+n]) for n=0 to n=1000.

Any ideas on how one can do that without a Cartesian product?

Best,

Kostas

---------- Forwarded message ----------

From:**Mats Zachrison** <mats.zachrison@ericsson.com>

Date: Tue, Mar 31, 2015 at 9:21 AM

Subject:

To: Kostas Tzoumas <kostas@data-artisans.com>, Stefan Avesand <stefan.avesand@ericsson.com>

Cc: "stephan@data-artisans.com" <stephan@data-artisans.com>

From:

Date: Tue, Mar 31, 2015 at 9:21 AM

Subject:

To: Kostas Tzoumas <kostas@data-artisans.com>, Stefan Avesand <stefan.avesand@ericsson.com>

Cc: "stephan@data-artisans.com" <stephan@data-artisans.com>

As Stefan said, what I’m trying to achieve is basically a nice way to do a correlation between two large time series. Since I’m looking for an optimal delay
between the two series, I’d like to delay one of the series x observations when doing the correlation, and step x from 1 to 1000.

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Some pseudo code:

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For (x = 1 to 1000)

Shift Series A ‘x-1’ steps

Correlation[x] = Correlate(Series A and Series B)

End For

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In R, using cor() and apply(), this could look like:

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shift <- as.array(c(1:1000))

corrAB <- apply(shift, 1, function(x) cor(data[x:nrow(data), ]$ColumnA, data[1:(nrow(data) - (x - 1)), ]$ColumnB))

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Since this basically is 1000 independent correlation calculations, it is fairly easy to parallelize. Here is an R example using foreach() and package doParallel:

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cl <- makeCluster(3)

registerDoParallel(cl)

corrAB <- foreach(step = c(1:1000)) %dopar% {

corrAB <- cor(data[step:nrow(data), ]$ColumnA, data[1:(nrow(data) - (step - 1)), ]$ColumnB)

}

stopCluster(cl)

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So I guess the question is – how to do this in a Flink environment? Do we have to define how to parallelize the algorithm, or can the cluster take care of that
for us?

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And of course this is most interesting on a generic level – given the environment of a multi-core or –processor setup running Flink, how hard is it to take
advantage of all the clock cycles? Do we have to split the algorithm, and data, and distribute the processing, or can the system do much of that for us?

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