用可调的生成模型实现时间序列多步预测的可靠置信区域
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
- 将条件归一化流与置信预测结合，构建灵活的预测区间
- 能处理多模态分布，生成不连通的预测区域，提升效率
- 适合需要高可靠性预测的金融、气象等时序任务
置信预测为机器学习模型的不确定性提供了强大框架，可保证有限样本下的预测集有效性。尽管其易于应用于非概率模型，但将其应用于概率生成模型（如归一化流）仍具挑战。本文提出一种新方法，对条件归一化流进行置信化，专门解决多步时间序列预测中获取预测区域的问题。该方法利用归一化流的灵活性，可生成可能不连通的预测区域，在存在多模态预测分布时显著提升预测效率。
原文摘要 · Abstract (English)
Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models, enabling the construction of prediction sets with finite-sample validity guarantees. While easily adaptable to non-probabilistic models, applying conformal prediction to probabilistic generative models, such as Normalising Flows is not straightforward. This work proposes a novel method to conformalise conditional normalising flows, specifically addressing the problem of obtaining prediction regions for multi-step time series forecasting. Our approach leverages the flexibility of normalising flows to generate potentially disjoint prediction regions, leading to improved predictive efficiency in the presence of potential multimodal predictive distributions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。