# Confusion regarding what constitutes a feature in a LSTM?

I have a Time Series problem, where I am trying to predict a single output at time $$t$$, $$y_t$$, given the $$2$$ previous time steps; $$X_{t-2}, X_{t-1}$$.

Let's just look at one observation for simplicity.

At a given time step $$t$$, I have $$3$$ features and a single output. Let's say $$[a_t, b_t, c_t, y_t]$$, where $$a_t, b_t, c_t$$ are my features, and $$y_t$$ is my output (the value I want to predict).

So, If I want to predict $$y_t$$ given the previous $$2$$ timesteps, this would look like

$$[ [a_{t-2}, b_{t-2}, c_{t-2}, y_{t-2}],\\ [a_{t-1}, b_{t-1}, c_{t-1}, y_{t-1}], \\ [a_{t}, b_{t}, c_{t}, ?]]$$

I don't have a value for $$y_t$$ here, and I need to pass in $$4$$ features to my $$X_t$$, so how does this work exactly?

At time $$t$$, I am again aware of my features $$a_t, b_t, c_t$$, and I want to predict $$y_t$$. But if I am only looking at the previous 2 timesteps here, I don't understand how the LSTM knows anything about the features at the current time step?

[[$$a_{t-2}$$, $$b_{t-2}$$, $$c_{t-2}$$, $$y_{t-1}$$],
[$$a_{t-1}$$, $$b_{t-1}$$, $$c_{t-1}$$, $$y_t$$]]
Where $$y_t$$ is the thing you are trying to predict.
[$$a_{t-1}$$, $$b_{t-1}$$, $$c_{t-1}$$, $$y_{t-1}$$, $$y_t$$]