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Hello!
Is there support for fitting a model with a certain issue/creation time n time steps before the prediction target window? I.e. I want to make the prediction at time t for time steps [t+12, t+12+24] (make prediction at at lunch time for the next days 24 hours).
I could of course extend forecast_length to go far enough into the future, but then I'd like the validation to ignore the first 12 time steps of this forecast length.
The text was updated successfully, but these errors were encountered:
Assuming I understand correctly, your best plan is to resample the data to a 12 hour frequency instead of hourly frequency. Specify frequency = '12h' or resample before passing into the model. Because yes, it definitely helps focus the model only on the points you care about rather than everything else.
Another option that can help a little is to focus on using the 'ewmae_weighting' in the metric_weighting. This doesn't ignore the first n points but it does weight the accuracy to the end more than the beginning.
No, I don't have support for specifying a 'custom' number of required steps ahead. This is because some models use a forward rolling approach to forecasting (n+1 used for n+2 which is used for n+3, etc) which means you can't really skip steps easily.
I could add per timestep weighting to the metrics, if you really think that would help your use case.
Hello!
Is there support for fitting a model with a certain issue/creation time
n
time steps before the prediction target window? I.e. I want to make the prediction at timet
for time steps[t+12, t+12+24]
(make prediction at at lunch time for the next days 24 hours).I could of course extend
forecast_length
to go far enough into the future, but then I'd like the validation to ignore the first 12 time steps of this forecast length.The text was updated successfully, but these errors were encountered: