Comments (1)
I found the answer myself.
For temporal prediction it is something like
R1 = reconstruct(train1[1:end-1], em)
R2 = reconstruct(train2[1:end-1], em)
R3 = reconstruct(train3[1:end-1], em)
R_combined = vcat(R1, R2, R3)
tree = KDTree(R_combined)
train_combined = vcat(train1, train2[1+τmax:end], train3[1+τmax:end])
prediction_starter = "this needs to be passed explicitly or it is taken from the end of train3"
If we prepare the training sets in this way, we do not need to change the prediction algorithm.
from timeseriesprediction.jl.
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from timeseriesprediction.jl.