arXiv:2605.30213v1 Announce Type: new Abstract: Continuous-time models are a natural choice for irregular and asynchronous data. A central design choice is how to embed discrete observations into continuous time. Interpolation- and imputation-based embeddings reconstruct a continuous observation path, making the model sensitive to the choice of reconstruction. We show that this reconstruction step is unnecessary; under mild conditions, compact-set universality on the model input space transfers to the data space whenever the embedding from data to input is continuous and injective. Guided by t
This research addresses a fundamental challenge in processing irregular time-series data, a growing problem with the proliferation of real-world sensors and asynchronous data streams, indicating ongoing refinement in AI model development.
Improved methods for handling asynchronous and irregular data enhance the robustness and applicability of continuous-time AI models, expanding their utility across dynamic and real-world scenarios.
This work suggests that the critical reconstruction step in embedding irregular data may be unnecessary under certain conditions, potentially simplifying model design and improving efficiency.
- · AI researchers
- · Developers of real-time systems
- · Generative AI models
- · Logistics and supply chain management
- · Interpolation-reliant data processing methods
- · Models overly sensitive to reconstruction choices
More accurate and efficient continuous-time models for irregular data emerge, leading to better predictions and analyses.
This improved data handling could reduce the computational burden and data preparation steps for certain AI applications.
It might enable the deployment of AI in more diverse and complex real-world environments where data irregular is the norm, such as autonomous systems or complex industrial monitoring.
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Read at arXiv cs.LG