Chaos and unpredictability are traditionally synonymous, yet recent advances
in statistical forecasting suggest that large machine learning models can
derive unexpected insight from extended observation of complex systems. Here,
we study the forecasting of chaos at scale, by performing a large-scale
comparison of 24 representative state-of-the-art multivariate forecasting
methods on a crowdsourced database of 135 distinct low-dimensional chaotic
systems. We find that large, domain-agnostic time series forecasting methods
based on artificial neural networks consistently exhibit strong forecasting
performance, in some cases producing accurate predictions lasting for dozens of
Lyapunov times. Best-in-class results for forecasting chaos are achieved by
recently-introduced hierarchical neural basis function models, though even
generic transformers and recurrent neural networks perform strongly. However,
physics-inspired hybrid methods like neural ordinary equations and reservoir
computers contain inductive biases conferring greater data efficiency and lower
training times in data-limited settings. We observe consistent correlation
across all methods despite their widely-varying architectures, as well as
universal structure in how predictions decay over long time intervals. Our
results suggest that a key advantage of modern forecasting methods stems not
from their architectural details, but rather from their capacity to learn the
large-scale structure of chaotic attractors.Comment: 5 pages, 3 figure