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

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Timeflies is a novel unified framework for time series forecasting that addresses the challenge of highly incomplete and irregular real-world data by jointly modeling future observational existence and value estimation. Unlike prior methods that assume future observation timestamps are known, Timeflies explicitly predicts whether an observation will occur before estimating its value. It features a dual-stream architecture with reliability-aware embedding, observation-guided dependency modeling, and a joint prediction head. The framework was evaluated on Shadow, a new benchmark combining public and real-world industrial datasets with natural missingness, and introduces the Observation-Value Joint Entropy (OVJE) metric. Timeflies consistently outperforms existing methods, showing significant gains, especially under high data sparsity.

Key takeaway

For Machine Learning Engineers developing time series forecasting models for real-world, irregular data, you should integrate explicit future observability inference into your pipeline. Relying on traditional methods that assume known future timestamps will lead to brittle performance, especially with high data sparsity. Consider adopting a dual-stream approach like Timeflies to jointly predict both observation existence and value, ensuring more robust and practically relevant predictions.

Key insights

Forecasting irregular time series requires jointly predicting observation existence and value, not just value.

Principles

• Missingness is a temporal signal, not just a data flaw.
• Future observability is an indispensable gate for reliable numerical prediction.

Method

Timeflies uses parallel value and observation streams, coupled by reliability-aware patch embedding, observation-guided attention, and a dual prediction head, optimized with a joint loss function.

In practice

• Use the Shadow benchmark for realistic evaluation.
• Employ Observation-Value Joint Entropy (OVJE) for coupled predictability assessment.

Topics

• Time Series Forecasting
• Missing Data
• Observability Inference
• Neural ODEs
• Timeflies Framework
• Shadow Benchmark
• Observation-Value Joint Entropy

Code references

• ant-intl/Timeflies

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.