# RNN Time Series Footfall - How do I construct this RNN?

I have daily time-series data, which tells me the rain fall & foot fall at a certain shop on that day. Now, I want to predict the foot fall at time $$t$$, given the previous $$2$$ observations.

As I'm dealing with time-series data, I thought I could use a RNN, feeding in the previous $$2$$ observations.

Now, I want it to learn the dependency between rainfall & footfall (i.e, if it's raining, there will be less footfall), and I want it to be able to look at previous rainfall values in order to gauge the current rainfall.

Let's just consider one observation for the time being.

Let $$r_t$$ be the rain value at time step $$t$$ and $$y_t$$ be the footfall at time $$t$$, $$y_t$$ is what I want to predict.

I thought I could construct an input like:

$$[[r_{t-2}, y_{t-2}],\\ [r_{t-1}, y_{t-1}]]$$

in order to predict $$y_t$$. But, given I'm at timestep $$t$$ and I know the rainfall $$r_t$$, it seems like the RNN has no way of accessing this information? If I know it is raining at timestep $$t$$, then how do I feed the model this?

I have had a look at parallel series but these aren't really what I'm looking for, as I'm using the previous $$y_t$$ as a feature here essentially.

Is there a way of structuring this to give me what I want?