Causal Time Series
Summary
Causal structure appears as both a data-generation assumption and a reasoning task in the time-series cluster.
What The Wiki Currently Believes
- CauKer combines Gaussian-process kernel composition with structural causal models to generate synthetic, causally coherent time series.
- TimeOmni-1 includes causality discovery as one of the perception capabilities in TSR-Suite.
Evidence
CauKer uses causality to create pretraining data; TimeOmni-1 uses causality as a reasoning/evaluation target. Together they suggest causal structure is not optional if the goal is temporal understanding rather than curve fitting.
Open Questions
- How much causal correctness is needed for synthetic pretraining to transfer?
- Can models learn causality from synthetic templates without overfitting to template artifacts?