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I'm interested in predicting a temporal variable $X(t)$ using a recurrent neural network. The target $X(t)$ depends on both itself at previous times as well as another temporal function $W(t)$. I always know and can compute $W(t)$ for any given time. My goal with the RNN is to build a model for $X(t)$.

I'm familiar with the general idea of a RNN, but have never used one in practice. I'd like to get an answer to a basic question about RNN training before I spend more time understanding more about the underlying details. If a RNN approach is incompatible with what I'm trying to do I'd like to know it upfront.

My question is, can I train the RNN if my data set only contains $X(t)$ at certain multiples of the time discreteness I'd like an answer to? For example, I want the RNN to produce an output for $X(t)$ for every time step $\Delta t$. If my data set for training only contains $X$ values at ${X(0), X(2 \Delta t), X(5 \Delta t), ...}$ and so on, does that pose a fundamental problem in training? I have $W(t)$ for all times (e.g., $W(i*\Delta t)$ for all $i$), but simply do not have the corresponding $X(t)$ values for all times.

I don't know if this is absolutely not a problem for training a RNN, if this is something which is a problem but for which there are workarounds, or whether this is a fundamental problem that means that a RNN might not be used as stated above.

Could someone please shed some light on this before I spend too much time pursuing this approach?

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