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I want to train the network based on two sets of data. For example, I want the network to predict the humidity based on past humidity trends AND past temperature trends. In this case, how should I organize the input layer? I would think that for just one time-series, I would use a regular sliding window on the series. With two series, do I just present both windows (from the humidity and temperature series) to the input layer that is twice the size of the window? if not, how else can I configure the input layer so that it doesn't confuse between the two sets of training data?

Do I just let the network sort this out by itself or is there a preferred method of presenting two (or more) sets of training data to the network? Thanks.

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  • $\begingroup$ Could an outer product layer be useful? It seems like it would get every variable in one vector combined with every variable in another. $\endgroup$ – Chris Anderson Dec 18 '16 at 0:48
  • $\begingroup$ Could you elaborate on this outer product layer? I'm just finding presenting two series as one a little counter-intuitive and am wondering if I'm missing something obvious here. What do you have in mind - some form of pre-processing? $\endgroup$ – Daniel Wee Dec 18 '16 at 2:08
  • $\begingroup$ Layer is a silly word I guess. It's just the outer product of two vectors. Really it seems like you need an RNN of some kind. Maybe you could preprocess the two to combine them into a single sequence though. $\endgroup$ – Chris Anderson Dec 18 '16 at 2:13
  • $\begingroup$ I intend to feed this into an LSTM type NN. As for submitting the vector product - won't that destroy some of the data features that might be useful for the NN? $\endgroup$ – Daniel Wee Dec 18 '16 at 2:19
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The whole idea of NN is that it learns by itself to give the appropriate weights and biases to the input neurons, so yes - just give your network the 2 series and it should return the appropriate weight and bias for each neuron in the input.

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  • $\begingroup$ Thanks beniev. I suspected as much but wondered if there were some optimisations that could be done eg: should I present the series in concatenated fashion or in interleaved fashion? $\endgroup$ – Daniel Wee Dec 18 '16 at 0:23

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