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 -> 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 -> 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?

Hi @Tyler Nisonoff. 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.