论文标题
时间序列的自适应共形预测
Adaptive Conformal Predictions for Time Series
论文作者
论文摘要
预测模型的不确定性量化对于决策问题至关重要。共形预测是一个一般且理论上正确的答案。但是，它需要可交换的数据，不包括时间序列。尽管最近的作品解决了这个问题，但我们认为自适应保形推理（ACI，Gibbs和Cand {è}，2021年），用于分配缩换时间序列，是具有一般依赖性的时间序列的一个很好的过程。我们从理论上分析了学习率对可交换和自动回归案例中其效率的影响。我们提出了一种无参数的方法Agaci，该方法基于在线专家聚合以ACI的形式自适应。我们针对倡导ACI在时间序列中使用的竞争方法进行广泛的公平模拟。我们进行了一个真实的案例研究：电价预测。提出的聚合算法为日预测提供了有效的预测间隔。所有用于复制实验的代码和数据都可以使用。
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.
