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Here is my problem: I have input [x1,..,xt,n1,..,nt,1,2,...,t] where there is a missing timestep xi, and I use neighboring time series (found with KNN) n1,...,nt to add more features, as well as time embeddings at the end. I want to predict the missing timestep xi using this input with a simple DNN regressor. Is there any useful way to use the time embeddings? I want xi+1 and xi-1 to have the most influence on the prediction of xi. Would positional encoding work for example?

I am staying away from RNNs because I want a simple regressor and my time series is very short.

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