Adaptive Conformal Predictions for Time Series
This work addresses uncertainty quantification for time series forecasting, which is crucial for decision-making in domains like energy markets, though it is incremental as it builds upon existing ACI methods.
The paper tackles the challenge of applying conformal prediction to time series data, which violates the exchangeability assumption, by proposing AgACI, a parameter-free method that adaptively aggregates experts based on Adaptive Conformal Inference (ACI). The method demonstrates efficient prediction intervals in simulations and a real-world electricity price forecasting case study, showing improved performance over competing methods.
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{è}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.