I've been reading different papers which implements the Transformer for time series forecasting. Most of the them are claiming that the training time is significantly faster then using a normal RNN. From my understanding when training such a model, you can encode the input in parallel, but the decoding is still sequential unless you're using teacher-forcing.
What makes the transformer faster than RNN in such a setting? Is there something that I am missing?