nit: fix axis in quantile loss computation #312
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@jbial Thank you for the PR! We are aware of this issue but we did not "fix" this for posterity --- this is how Chronos-Bolt models were trained. In practice, this results in a rescaling of the loss which, while unpleasant, is not problematic from an optimization perspective. |
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Issue #, if available:
Description of changes:
Nit change of the shape in the quantile loss computation. The variable
loss(here) has shape (B, num quantiles, T), so to mean over the prediction horizon (match the comment on line 366), the dim should be -1 instead of -2.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.