Conditional Cauchy-Schwarz Divergence for Time Series Analysis: Kernelized Estimation and Applications in Clustering and Fraud Detection

TL;DR

This paper introduces a kernel-based method for estimating the conditional Cauchy-Schwarz divergence in time series, enabling improved clustering and fraud detection with stable, density-free measures.

Contribution

It develops a practical, stabilized kernel estimator for C-CSD tailored for time series analysis, with applications in clustering and fraud detection.

Findings

Effective in time series clustering with normalized mutual information evaluation.

Accurate fraud detection by contrasting global and local transaction mixture models.

Demonstrates stable estimation and strong performance on benchmark datasets.

Abstract

We study the conditional Cauchy-Schwarz divergence (C-CSD) as a symmetric and density-free measure for time series analysis. We derive a practical kernel based estimator using radial basis function kernels on both the condition and output spaces, together with numerical stabilizations including a symmetric logarithmic form with an epsilon ridge and a robust bandwidth selection rule based on the interquartile range. Median heuristic bandwidths are applied to window vectors, and effective rank filtering is used to avoid degenerate kernels. We demonstrate the framework in two applications. In time series clustering, conditioning on the time index and comparing scalar series values yields a pairwise C-CSD dissimilarity. Bandwidths are selected on the training split, after which precomputed distance k-medoids clustering is performed on the test split and evaluated using normalized mutual information. In fraud detection, conditioning on sliding transaction windows and comparing the magnitude of value changes with categorical and merchant change indicators, each query window is scored by contrasting a global normal reference mixture against a same account local history mixture with recency decay and change flag weighting. Account level decisions are obtained by aggregating window scores using the maximum value. Experiments on benchmark time series datasets and a transactional fraud detection dataset demonstrate stable estimation and effective performance under a strictly leak free evaluation protocol.