How to input time to a neural network? I am working with a time-series, and believe that time in seconds since start is a useful feature. I am however not sure how to best input this to my recurrent neural network.
I don't think the naive approach of just entering the naive value is the best way, and I also don't think that letting the RNN keep track of it on its own is good either.
What has been done before, and what considerations should I keep in mind if I want to use time as an input variable?

Clarification
Let's say that at each time step, t (seconds as integers, ~0-1800), I have a feature vector x.
I believe knowing the value of t is useful i.e. how much time has elapsed since the start of a process. How do I best input t to the network? I.e. is there something better than concat(x, t)?
 A: You are essentially applying a recurrent neural network to solve a Markov problem. The neural network can be trained to learn a so-called hidden Markov model, in your time data. You try to predict the probabilities of the possible next states at time $t$, given the current state $t-1$, the previous states (represented by feature variables), and state information: $t-2$, $t-3$, $\ldots$.
One first question you need to address, is whether the state at $t-2$ has any extra predictive power as to which next state is being reached. As the underlying mechanism is assumed to be unknown, you ought to experiment with this - adding or retracting historic, previous state information from your recurrent neural network.
There are basically two alternative strategies to follow: They both involve training the RNN to convolve along the time/state axis. According to one strategy, you ridirect the output vector of the RNN to special input units, which exist alongside the regular feature-input nodes. According to the other strategy, you provide the vector of hidden-node values to the special input units. The implicit assumption of both these approaches is that you are dealing with a first-order Markov chain. State-information before $t-1$ is completely incorporated into the current state. 
Keep a subset of your data as a separate test set.
