Comments (4)
Hi @MBelniak,
The selection of the depth of the network needs to accommodate the availability of the data.
In our empirical findings, smaller networks tend to have a better performance on smaller datasets.
In case that you are interested in seeing the effect of the the depth in the network, in the hyperparameter search can be easily controlled by switching these lines:
https://github.com/cchallu/nbeatsx/blob/main/src/hyperopt_nbeatsx.py#L60-L61
Greetings
from nbeatsx.
BTW I've just realized that for NBeats-G it's the same whether we use 30 stacks with 1 block each or 1 stack with 30 blocks. The architecture stays the same :) So the only valid question is about your architecture and the configuration of stacks.
from nbeatsx.
A good library to observe your model configuration is this:
https://github.com/sksq96/pytorch-summary
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Hi @kdgutier.
So i guess I just need to play around with the number of stacks, their order etc., like in the grid-search. Thanks.
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Related Issues (16)
- [Question] SELU weights and dropout HOT 1
- nothing
- n_val_weeks in the paper and code do not match HOT 3
- `run_test_nbeatsx` confusion HOT 2
- Missing import statements in nbeats.py HOT 1
- insample_mask and outsample_mask HOT 1
- Can NbeatsX be used for multiple-variate datasets?
- datasets of nbeatsx can not download HOT 2
- [Question] dropout_prob_exogenous
- Non-EPF Example HOT 4
- Number of stacks confusion
- Forecasting with long output dimension HOT 1
- Hyperparameter files HOT 5
- Any advice, please. HOT 1
- FCNN realization doesn't take exogenous variables as arguments HOT 2
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