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?
