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.

翻译：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呈现高度不完整性与不规则性，使得可靠预测面临根本性挑战。现有方法已从先插值再预测的流水线范式演进至连续时间模型（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，其在推理阶段仍隐含着理想化的先知假设：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应用价值，因为在众多真实系统中，更基础的问题不仅关乎未来数值如何，更在于有效观测是否会发生。本文提出Timeflies统一框架，将预测重新定义为未来可观测性推断与数值估计的联合问题。为显式建模观测动态与状态演变间的交互作用，Timeflies采用观测流与数值流双流架构，通过三个专用模块（可靠性感知嵌入、观测引导的依赖建模、联合预测）实现耦合。我们进一步构建了Shadow基准数据集，融合公开数据集中的天然缺失模式与真实工业数据，并提出观测-数值联合熵指标综合评估这种耦合可预测性。大量实验表明，Timeflies持续优于现有方法，突显了在含缺失值的时间序列预测中显式建模未来可观测性的重要性。代码与数据集见此链接：https://github.com/ant-intl/Timeflies。