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

Excerpt

arXiv:2606.13571v2 Announce Type: replace 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 oracl