Having trouble thinking through how to fit a curre...
# neural-forecast
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
as i do not yet have prices from 11am-midnight I could extend the horizon to be
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?
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
hours when fitting the model?
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
to be 24h instead of the horizon.
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