潜变量驱动的部分观测时间序列插补与预测（LIFTS）

中文摘要 AI 辅助

部分观测的多元时间序列带来了两个相互关联的挑战：恢复缺失测量值，以及在观测模式具有信息性时预测未来轨迹。现有方法通常假设缺失是可忽略的，或者仅将掩码用作辅助输入。我们提出了LIFTS（潜变量驱动的部分观测时间序列插补与预测），这是一个概率框架，其中潜过程共同控制完整的测量轨迹和观测掩码，使得观测模式能够为潜状态推断提供信息，从而同时服务于预测和插补。LIFTS将灵活的神经网络参数化与前向-后向架构相结合：前向传递执行滤波和自回归预测，而后向传递执行平滑和条件多重插补。模型和推断组件通过掩码能量评分和自掩码进行联合训练。我们为该一般框架建立了前向-后向分布表示，并针对一个显式结构子类提供了全定律识别和一致性保证。模拟实验和对PhysioNet 2019的应用表明，LIFTS提供了准确的点预测、改进的分布准确性和校准良好的预测区间；它在训练数据有限时尤其优于竞争方法，并且其推断速度显著快于基于扩散的基线方法。

英文摘要

Partially observed multivariate time series pose two coupled challenges: recovering missing measurements and forecasting future trajectories when observation patterns are informative. Existing methods often assume ignorable missingness or use the mask only as an auxiliary input. We propose LIFTS (Latent-driven Imputation and Forecasting for partly observed Time Series), a probabilistic framework in which a latent process jointly governs the complete measurement trajectory and observation mask, allowing observation patterns to inform latent-state inference for both forecasting and imputation. LIFTS combines flexible neural-network parameterizations with a forward-backward architecture: the forward pass performs filtering and autoregressive forecasting, while the backward pass performs smoothing and conditional multiple imputation. The model and inference components are trained jointly using a masked energy score and self-masking. We establish forward-backward distributional representations for the general framework and, for an explicit structural subclass, provide full-law identification and consistency guarantees. Simulations and an application to PhysioNet 2019 show that LIFTS provides accurate point predictions, improved distributional accuracy, and well-calibrated predictive intervals; it outperforms competing methods particularly when training data are limited and has substantially faster inference than the diffusion-based baseline.

发表机构

• Columbia University（哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。