Conformal prediction for time series

Understand

We develop a general framework for constructing distribution-free prediction intervals for time series.

• Theoretically, we establish explicit bounds on conditional and marginal coverage gaps of estimated prediction intervals, which asymptotically converge to zero under additional assumptions.
• We obtain similar bounds on the size of set differences between oracle and estimated prediction intervals.
• Methodologically, we introduce a computationally efficient algorithm called \texttt{EnbPI} that wraps around ensemble predictors, which is closely related to conformal prediction (CP) but does not require data exchangeability.