LSTM: Taking previous output values as feature As far as I know, there is practically no limit on the number of dimensions of input feature for LSTM. And it apparently can learn the sequence of data.

My question is does LSTM by nature, also take previous output values besides feature vector as new feature element for the next output values? i.e. If we have:

[t]  -&gt; [x1, ..., xn] [y_t]
[t+1] -&gt; [x1, ..., xn] [y_(t+1)]

Is it necessary to manipulate data like this

[t]  -&gt; [x1, ..., xn, y_(t-1)] [y_t]
[t+1] -&gt; [x1, ..., xn, y_t]    [y_(t+1)]

or LSTM is already handling this for us?
LSTMs handle this for you. 

You can search for some tutorial on the LSTMs that explains how they works. One that I like is this.