Temporal horizons in forecasting: a performance-learnability trade-off

Pau Vilimelis Aceituno, Jack William Miller, Noah Marti +2

When training autoregressive models to forecast dynamical systems, a critical question arises: how far into the future should the model be trained to predict? Too short a horizon m…