Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
arXiv:2606.13571v1 Announce Type: cross 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 i
This research addresses a fundamental limitation in time series forecasting, which is critical for real-world applications where data is inherently incomplete and irregular.
Improving forecasting accuracy for incomplete real-world data has broad implications for operational efficiency, risk management, and strategic planning across various sectors reliant on time series analysis.
Traditional forecasting models will be challenged by approaches that explicitly account for the existence and evolution of observations, potentially leading to more robust and reliable predictions in dynamic environments.
- · AI/ML researchers
- · Logistics and supply chain companies
- · Predictive maintenance sectors
- · Financial modeling
- · Legacy time series forecasting methods
- · Companies relying on clean, complete datasets for predictions
More accurate predictions from incomplete real-world data will enable better decision-making in diverse applications.
The improved reliability of AI-driven forecasts could accelerate automation and agentic systems in sectors previously hampered by data uncertainty.
Advances in handling irregular time series data could lead to new types of sensor deployment and data collection strategies, optimizing for information density rather than continuous streams.
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Read at arXiv cs.AI