Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting

2026-06-11

AI Summary

Real-world time series are often highly irregular and severely missing due to sensor dormancy or transmission delays. Existing methods typically assume future observation times are known, overlooking the critical question of whether future values will even be observable. This work proposes Timeflies, a novel framework that reframes time series forecasting as a joint task of inferring future observability and estimating numerical values. Timeflies employs a dual-stream architecture—comprising an observation stream and a value stream—augmented with reliability-aware embeddings, observation-guided dependency modeling, and continuous-time dynamics to explicitly co-learn the presence of observations and the evolution of underlying states. Evaluated on the newly introduced Shadow benchmark and OVJE metric, Timeflies significantly outperforms existing approaches, demonstrating that jointly modeling future observability is essential for improving forecasting performance under substantial missingness.

Abstract

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.

Problem

• time series forecasting
• missing data
• observability inference
• irregular sampling
• joint prediction

Innovation

• joint forecasting
• observational existence
• time series with missingness
• Neural ODEs
• uncertainty-aware prediction