Closed MLfreakPy closed 2 months ago
@cchallu: as you suggested I submitted an issue for the random early stopping set here (as in the NBEATSx paper). If there are any questions please let me know and I will be happy to provide more context.
@steffenrunge I think you can use cross-validation to perform what you want. Cross-validation will at the end refit the model with the entire training data based on the best hyperparams.
Let me know if that solves the problem for you.
This issue has been automatically closed because it has been awaiting a response for too long. When you have time to to work with the maintainers to resolve this issue, please post a new comment and it will be re-opened. If the issue has been locked for editing by the time you return to it, please open a new issue and reference this one.
Description
Issue: When deploying the
nbeatsx
model and making use of all the available data up to that date astraining set
(which should increase performance), I do not have any other future values. So how do I know after how many training epochs to stop?Note: I am fairly new in the game and might be overseeing something, so happy to take on any correction.
Thank you for your consideration and continuous work on improving the nixtla univserse and, with that, the state of forecasting!
Use case
Context: In the way
early stopping
is currently implemented, I do not have the possibility to make use of all available data for training. This is striking as often the most recent data (say last week) has the strongest predictive power to forecast the next period (say next week). The original nbeatsx paper here proposes a random early stopping set as a solution (being a random period over the updated training set). This could be a very useful feature to permit the deployment of nbeatsx model in production with early stopping and use of all the available data as training data.