Comments (1)
Thanks for the question, @gorold. l_s
is the length of the sequence passed to the LSTM, which will use that sequence to start generating predictions for timesteps > l_s
. Some length l_s
of input sequence is required to generate future predictions, and because of this the length of the resulting (smoothed) error sequence (self.e
, self.e_s
) will be equal to len(observations) - l_s
.
The portion of errors.py
that you referenced isn't intended to restore the sequence to the original observation length. The conversion of early errors to a mean value is to account for large error spikes resulting from the initial predictions that are not smoothed out by the exponentially-weight moving average. If you remove the lines you reference above and re-run you can see this effect.
The additional delta of 10 from the original length of the observations is due to n_predictions
. We need 10 future test
values to generate the loss that is backpropagated during training (see #24 (comment)) and evaluate normalized loss during test. This isn't a requirement for inference however, and I'd welcome a PR to address this.
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