  Recurrent neural networks and sequence to sequence models require a
predetermined length for prediction output length. Our model addresses this by
allowing the network to predict a variable length output in inference. A new
loss function with a tailored gradient computation is developed that trades off
prediction accuracy and output length. The model utilizes a function to
determine whether a particular output at a time should be evaluated or not
given a predetermined threshold. We evaluate the model on the problem of
predicting the prices of securities. We find that the model makes longer
predictions for more stable securities and it naturally balances prediction
accuracy and length.
