[D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)

I am trying to predict many steps in the future using an LSTM.

The first doubt that came across my mind is what's the correct way to predict multiple timesteps. This is what I thought:
* Predicting only a single timestep for every forward pass of the model, then reusing the last output for the successive input. Sort of a self-feeding infinite loop.
* Predicting a fixed time window having shape (future data points, targets). I have done this by …

every future implementation lstm machinelearning mind multiple prediction thought timeseries weather