title: Multistep forecasting using LSTM with pytorch
body: <p>I am quite new to programming neural networks and especially on using Pytorch. Therefore, I have a question on how to make multistep ahead forecasts using this model.</p>
  <p>I want to predict time series by using LSTM. I have implemented code where I use sliding windows, however, it seems that my model is able on making one step ahead forecasts, but I do not fully understand on how to generate multistep ahead forecast.</p>
  <p>Do you then have to make sliding windows, with for example input sequence length 20 and label length 5? And then make the output of the LSTM model also of length 5? However, it seems that the model is then not good on picking up the sequence.</p>
  <p>Or should you use rolling forecasting, where you use the last forecast as new input for the next forecast.</p>
  <p>Can someone please help me on how I can perform multistep ahead forecasts using LSTM in Pytorch? Thank you!</p>