  Uncertainty quantification of predictive models is crucial in decision-making
problems. Conformal prediction is a general and theoretically sound answer.
However, it requires exchangeable data, excluding time series. While recent
works tackled this issue, we argue that Adaptive Conformal Inference (ACI,
Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a
good procedure for time series with general dependency. We theoretically
analyse the impact of the learning rate on its efficiency in the exchangeable
and auto-regressive case. We propose a parameter-free method, AgACI, that
adaptively builds upon ACI based on online expert aggregation. We lead
extensive fair simulations against competing methods that advocate for ACI's
use in time series. We conduct a real case study: electricity price
forecasting. The proposed aggregation algorithm provides efficient prediction
intervals for day-ahead forecasting. All the code and data to reproduce the
experiments is made available.
