TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
arXiv:2508.17519v3 Announce Type: replace Abstract: Handling missing data in time series classification remains a significant challenge in various domains. Traditional methods often rely on imputation, which may introduce bias or fail to capture the underlying temporal dynamics. In this paper, we propose TANDEM (Temporal Attention-guided Neural Differential Equations for Missingness), an attention-guided neural differential equation framework that effectively classifies time series data with missing values. Our approach integrates raw observation, interpolated control path, and continuous late
This research is emerging now due to the increasing volume and complexity of time series data, coupled with continued advancements in neural differential equations and attention mechanisms in AI.
Improved handling of missing data in time series classification has broad implications for reliability and accuracy in critical AI applications, accelerating progress in various data-driven fields.
The ability to accurately classify time series data with missing values without relying on potentially biased imputation methods will lead to more robust and trustworthy AI models.
- · AI researchers
- · Healthcare providers
- · Financial analysts
- · Autonomous systems developers
- · Traditional imputation software providers
- · Sectors reliant on heavily pre-processed and imputed time series data
More accurate and resilient AI models across various time series-dependent applications.
Reduced need for extensive data cleaning and pre-processing efforts for time series data, streamlining AI development.
Accelerated deployment of AI in domains with inherently noisy or incomplete real-world time series data, potentially broadening AI's application frontier.
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Read at arXiv cs.LG