论文标题
多步时时间序列预测的Copula共形预测
Copula Conformal Prediction for Multi-step Time Series Forecasting
论文作者
论文摘要
准确的不确定性测量是建立强大而可靠的机器学习系统的关键步骤。共形预测是一种无分配的不确定性量化算法，它以其易于实施，统计保证保证和基础预报者的多功能性而受欢迎。但是，时间序列的现有共形预测算法仅限于单步预测，而无需考虑时间依赖性。在本文中，我们提出了一种用于多变量，多步骤时间序列预测的Copula共形预测算法。我们证明Copulacpts具有有限的样本有效性保证。在几个合成和现实世界中的多元时间序列数据集中，我们表明，与现有技术相比，Copulacpts为多步预测任务产生更校准和尖锐的置信区间。
Accurate uncertainty measurement is a key step to building robust and reliable machine learning systems. Conformal prediction is a distribution-free uncertainty quantification algorithm popular for its ease of implementation, statistical coverage guarantees, and versatility for underlying forecasters. However, existing conformal prediction algorithms for time series are limited to single-step prediction without considering the temporal dependency. In this paper, we propose a Copula Conformal Prediction algorithm for multivariate, multi-step Time Series forecasting, CopulaCPTS. We prove that CopulaCPTS has finite sample validity guarantee. On several synthetic and real-world multivariate time series datasets, we show that CopulaCPTS produces more calibrated and sharp confidence intervals for multi-step prediction tasks than existing techniques.
