Is time series multi-step ahead forecasting a sequence to sequence problem?
Seq2Seq architecture can definitely be used for time series problem. The only twist is that you will need a linear layer on top of your decoder to project the outputs to the required size (for example, 1 for univariate).

The stepwise forecast approach can be used for short sequences but because any biases are compounded using this approach, it is not good for longer sequences.

For example, if you have a sequence where the value is constant at each time step $x_{i+1} = x_i$, but you model learned to do $x_{i+1}=1.01x_i$ instead (which is highly likely with gradient descent algorithm). For $t=10$, the target value will be $1^{50} = 1$, however you model will predict $1.01^{50}=1.64$. 

Thus a 1% single step error results in a 64% difference in 50 steps.