FAQ

A short list, growing as questions recur.

Why is likelihood the default, not CRPS?

A skater's target is a reusable predictive density, and densities are meant to be chained: each stage refines the residual of the last. Held-out log-likelihood is the score under which those refinements compose, the log-likelihood of a chained forecast being the base score plus the contribution of each stage. CRPS is a good score for a different target, coverage or threshold behaviour, and it is reported alongside as a diagnostic. It is a different contract, not a worse one. The full argument, with the worked example and where CRPS is the right choice, is in why skaters ranks by likelihood, by default.

Isn't the benchmark win just p-hacked?

Reasonable thing to ask of any leaderboard. Rather than take one slice on faith, the robustness explorer lets you cut the non-price benchmark any way you like — at random, by category, frequency, stickiness, martingality — and watch the per-series win-rate recompute live with a bootstrap band. A cherry-picked edge falls apart under reslicing; a real one stays high and tight. The price caveat is stated plainly too: on equity/fx/commodity returns GARCH-t wins, and we say so.

Do I need exogenous data or features for this to be useful?

Not to use it. Univariate distributional prediction is a composable component, not a whole pipeline: it runs on the series you have and plugs in before, during, or after the rest of a model. Exogenous structure enters as another stage. See Scope.

Why not just build a bespoke model?

For a single important series, do. The value here is autonomy and scale: thousands of streams, online, unattended, each returning a full distribution rather than a point. See Scope.