提出时间序列预测的可信置信区间生成方法，解决传统方法依赖分布假设的问题。
Conformal Prediction Algorithms for Time Series Forecasting: Methods and Benchmarking
- 放宽数据可交换性假设，适配时间序列的时序依赖特性
- 多步分割法在90%覆盖率下表现最优，区间宽度更优
- 适合需要高可靠性预测的工业级时间序列场景
可靠的时间序列不确定性量化至关重要，但传统方法常依赖严格的分布假设。分位数预测（Conformal Prediction, CP）作为无分布假设的框架，能提供具有严格理论保障的预测区间。然而，将CP应用于序列数据面临核心挑战：时间序列固有的时序依赖性违背了标准CP所依赖的数据可交换性假设。本文系统分析了应对该矛盾的主要算法路径：放宽可交换性假设的方法、将数据单元重新定义为独立时间序列集合的方法、显式建模预测残差动态的方法，以及能够适应分布漂移的在线学习算法。以AutoARIMA为基线预测器，在大规模月度销售数据集上评估了边际覆盖率、区间宽度与Winkler得分。结果表明，多步分割分位数预测方法达到90%覆盖率要求，并展现出最佳效率。
原文摘要 · Abstract (English)
Reliable uncertainty quantification is of critical importance in time series forecasting, yet traditional methods often rely on restrictive distributional assumptions. Conformal prediction (CP) has emerged as a promising distribution-free framework for generating prediction intervals with rigorous theoretical guarantees. However, applying CP to sequential data presents a primary challenge: the temporal dependencies inherent in time series fundamentally violate the core assumption of data exchangeability, upon which standard CP guarantees are built. This paper critically examines the main categories of algorithmic solutions designed to address this conflict. We survey and benchmark methods that relax the exchangeability assumption, those that redefine the data unit to be a collection of independent time series, approaches that explicitly model the dynamics of the prediction residuals, and online learning algorithms that adapt to distribution shifts to maintain long-run coverage. We use AutoARIMA as the base forecaster on a large-scale monthly sales dataset, evaluating marginal coverage, interval width, and the Winkler score. Our benchmark results show that multi-step split conformal prediction method meets the 90% coverage threshold and demonstrates the best efficiency.
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