open access paper https://arxiv.org/abs/2511.03877
Cross-channel prediction outperforms same-channel pre-
diction for early input-horizon, across all models. This is
consistent with correlations plots in Figure 3 and Figure 2....
...
We establish Lead-Lag Forecasting (LLF) as a formal prediction
problem, motivated by the gap between observed lead-lag dynam-
ics in important domains—including scientific and technological
impact—and popular time series forecasting benchmarks. We cat-
alyze research on LLF by curating and releasing two novel datasets:
arXiv papers and GitHub repositories. We establish lead-lag rela-
tionships in streams of activity data and provide baseline numbers
for several standard supervised machine learning methods on the
task of predicting a 5-year outcome from as little as one month of
observation. While our results demonstrate the existence of predic-
tive signal, we speculate that there are opportunities for innovation
to improve predictions.
Smells like Category Theory to me!
