New Framework Jointly Models Observation Likelihood and Values in Incomplete Time Series Forecasting
Researchers introduced Timeflies, a machine learning framework that simultaneously predicts whether future observations will occur and what their values will be in incomplete time series data. Current forecasting methods assume future observation timestamps are known in advance, an unrealistic assumption in many real-world systems with irregular or missing data. The approach addresses a fundamental gap in practical time series forecasting where data incompleteness is common due to sensor failures, transmission delays, and event-driven sampling.
The paper proposes Timeflies, a unified framework that reformulates time series forecasting as a joint problem of predicting future observability (whether valid data will be recorded) and value estimation (what that data will be). Real-world time series are often incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling. Existing methods, including Neural ODEs and continuous-time graph networks, implicitly assume that future observation timestamps are known in advance—a limitation that reduces practical applicability. Timeflies uses dual streams (observation and value) coupled through three modules for reliability-aware embedding, observation-guided dependency modeling, and joint prediction. The authors constructed Shadow, a benchmark combining natural missingness from public datasets with industrial data, and introduced the Observation-Value Joint Entropy (OVJE) metric for evaluation. Experiments demonstrate that Timeflies consistently outperforms existing methods.
What's missing
The paper does not discuss computational complexity or scalability requirements compared to baseline methods, nor does it provide details on the specific industrial datasets used in the Shadow benchmark or their characteristics.
What different sources said
- arXiv cs.AICenter
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
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