Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks
arXiv:2606.01602v1 Announce Type: new Abstract: Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust way to quantify dependence between heterogeneous data types, especially between continuous time series and discrete temporal event sequences. Existing approaches rely on ad hoc transformations or mutual-information estimators that are highly sensitive to quantization, repeated values, and event redundancy, leading to biased or unstable results in practice. We propose a nonparametric mutual information
This research addresses a fundamental limitation in temporal data analysis, which becomes increasingly critical as diverse, high-volume time-series and event data proliferate across various AI applications.
A robust method for quantifying dependence between heterogeneous temporal data types can significantly improve the accuracy and reliability of AI models, impacting domains from finance to healthcare and autonomous systems.
The ability to accurately estimate mutual information between continuous time series and discrete event sequences without bias from quantization or event redundancy will lead to more robust and explainable AI insights.
- · AI/ML researchers
- · Data scientists
- · Temporal data platform providers
- · Sectors reliant on time-series analytics
- · Ad-hoc analytical methods
- · Current biased mutual-information estimators
Improved understanding and modeling of complex systems with mixed data types.
Accelerated development of AI systems that can effectively integrate and learn from diverse real-world data streams.
New AI applications emerging from the enhanced ability to find meaningful relationships in previously intractable heterogeneous temporal data.
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Read at arXiv cs.LG