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Having trouble thinking through how to fit a current forecasting problem into the neuralforecast model:

Suppose every day at 10am, I want to predict tomorrows 24 hours worth of prices for some series (midnight to midnight).

It seems that currently the assumption is if I'm predicting time t -&gt; t+horizon, I have data up to t-1. But in this case, I only have data up to `t-14` as i do not yet have prices from 11am-midnight

I could extend the horizon to be `24+14` to predict 10am -&gt; 12am the next day, but then in training / fit we'd step by the horizon, instead of just stepping forward 10 days.

is there some way to fit this into the neuralforecast approach?

I found <https://github.com/Nixtla/neuralforecast/discussions/470>

which suggests filtering out prediction values for the first p results, but would this model step `p+h`  hours when fitting the model?

Hi <@U05778P619D>. This seems to be a particular case that it can't be currently handled. There are partial solutions, like increasing the forecast horizon and changing `step_size` to be 24h instead of the horizon.