存在先于价值：时间序列预测中观测存在性与状态演变的联合建模
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
AI总结：
提出Timeflies框架，联合建模未来观测是否发生（存在性）与数值估计，通过观测流和数值流耦合模块提升缺失值时间序列预测性能。
摘要：
现实世界的时间序列常因传感器休眠、传输延迟和事件驱动采样而高度不完整和不规则，使得可靠预测面临根本性挑战。现有方法已从插值后预测的流水线发展到连续时间模型，如神经常微分方程和连续时间图网络。尽管这些方法改进了历史不规则性的建模，但它们仍然在推理时依赖一个隐式的先知假设：未来有效观测的时间戳被假定为预先已知。这一假设限制了实际相关性，因为在许多现实系统中，更根本的问题不仅是未来值是多少，还包括是否会有有效观测发生。在本文中，我们提出Timeflies，一个统一的框架，将预测重新表述为未来可观测性推断和数值估计的联合问题。为了显式建模观测动态与状态演变之间的交互，Timeflies采用观测流和数值流，通过三个专用模块（可靠性感知嵌入、观测引导的依赖建模和联合预测）进行耦合。我们进一步构建了Shadow基准，该基准结合了来自公共数据集和真实工业数据的自然缺失，并引入观测-值联合熵（OVJE）指标来全面评估这种耦合的可预测性。大量实验表明，Timeflies始终优于现有方法，突显了在缺失值时间序列预测中显式建模未来可观测性的重要性。代码和数据集见https://this URL。
英文摘要：
Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, making reliable forecasting fundamentally challenging. Existing methods have evolved from impute-then-forecast pipelines to continuous-time models such as Neural ODEs and continuous-time graph networks. While these approaches improve the modeling of historical irregularity, they still rely on an implicit oracle assumption at inference time: the timestamps of future valid observations are presumed to be known in advance. This assumption limits practical relevance, since in many real systems the more fundamental question is not only what the future value will be, but also whether a valid observation will occur at all. In this paper, we propose Timeflies, a unified framework that reformulates forecasting as a joint problem of future observability inference and value estimation. To explicitly model the interaction between observation dynamics and state evolution, Timeflies adopts an observation stream and a value stream, coupled through three dedicated modules for reliability-aware embedding, observation-guided dependency modeling, and joint prediction. We further construct Shadow, a benchmark that combines natural missingness from public datasets with real-world industrial data, and introduce the Observation-Value Joint Entropy (OVJE) metric to comprehensively evaluate this coupled predictability. Extensive experiments show that Timeflies consistently outperforms existing methods, highlighting the importance of explicitly modeling future observability in time series forecasting with missing values. Code and dataset are available in https://github.com/ant-intl/Timeflies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
