Comments (3)
Hi @chododom, internally RNN has an output_chunk_length=1
, meaning during prediction when n>output_chunk_length
it will always perform auto-regression (consuming it's own predictions for future predictions) to forecast n
points.
training_length
just means how of those auto-regressive predictions it performs for one sample during training.
You can do two things:
- simply increase
n
from 4 to 8 when callingpredict()
- multiple training:
- train a
model1
with the lowertraining_length
- save the model with
model1.save(...)
- create a new
model2
object with the same hyperparameters but differenttraining_length
- load the weights from
model1
save intomodel2
withmodel2.load_weights(...)
(sincetraining_length
changed, for this to work you need to setskip_checks=True
, andload_encoders=False
). - train
model2
- train a
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@dennisbader Thank you for the advice! Do you think this will also work with the TFT model? If I understand correctly, the TFT has a built-in prediction horizon length.
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@adesso-dominik-chodounsky, any of Darts' torch models (neural networks) and regression models support auto-regressive predictions with n>output_chunk_length
. The only requirement is that if you use past/future_covariates
, you must know these far enough into the future to generate the n
prediction points.
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Related Issues (20)
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