The time series cluster kernel

Abstract

This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. In comparative experiments, we demonstrate that the TCK is robust to parameter choices and illustrate its capabilities of dealing with multivariate time series, both with and without missing data.