An autoregressive model uses previous output(s) as the new input. The Wikipedia article 18 gives you some more information.


From the same article:



Intertemporal effect of shocks
In an AR process, a one-time shock affects values of the evolving variable infinitely far into the future. […] Continuing this process shows that the effect of  never ends, although if the process is stationary 1 then the effect diminishes toward zero in the limit.

(I removed a large portion of the text due to the format.)
In DL setups teacher forcing might be used during training.
However, I’m not sure if there is any technique applicable during inference.

I’m not familiar a lot with autoregressive models in the DL domain. The “classical” AR models use a single output as the (scaled) input to the model, if I’m not mistaken (my work in the signal processing domain dates back a little further  ).