Generating strongly correlated data for numerical experiments

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1

Since real-life traffic traces are not readily available, for my simulations, I generate traffic traces as follows:

trace = []
nom = sample(collect(0.5:0.2,0.9), WeightVec([0.3,0.4,0.3]))
for i=1:100
  push!(trace, nom + rand(Normal(0,nom)))
end

I repeat this process for all commodities (c\in C) in the network thereby obtaining unique traces for every commodity. However, I would now like to generate strongly correlated traces. Is there a package in Julia that could help me generate such strongly correlated traces?

2

It is not clear what you are trying to do — is dnom a typo, or a variable that you did not initialize?

In any case, if you want series with correlated noise, you should just draw correlated variables (eg using a multivariate normal with non-diagonal variance matrix, or some more sophisticated hierarchical arrangement) and use them.

1 Like
3

Sorry about the typo. I donot want a temporally correlated trace but spatially correlated traces. Assuming we have two commodities c1 and c2, I want their respective traces to be strongly correlated with each other.

4

The following code generates two series that are strongly correlated with one another, but serially uncorrelated. Is that what you have in mind?

using Plots
T = 100
Sig = [1 0.9; 0.9 1]
P = chol(Sig)
x = randn(T,2)*P
x = x .+ [-2.0 2.0]
show(cov(x))
plot(x)
2 Likes
5

Thanks a lot. This is exactly what I wanted for my simulations.