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

Source: arXiv cs.LG — read the full report at the original publisher.

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