# Time series cluster kernels to exploit informative missingness and incomplete label information

arXiv: https://arxiv.org/abs/1907.05251 · HTML: https://mlsanity.com/paper/1907.05251/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://mlsanity.com/paper/2110.05357/) (2021)
- [On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit](https://mlsanity.com/paper/2107.10398/) (2021)
- [Modeling Regime Shifts in Multiple Time Series](https://mlsanity.com/paper/2109.09692/) (2021)
- [As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning](https://mlsanity.com/paper/2106.15577/) (2021)
- [Approaching adverse event detection utilizing transformers on clinical time-series](https://mlsanity.com/paper/2311.09165/) (2023)
- [Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations](https://mlsanity.com/paper/2504.17717/) (2025)

## Abstract (arXiv)

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, making it particularly well suited for unsupervised learning. However, TCK assumes missing at random and that the underlying missingness mechanism is ignorable, i.e. uninformative, an assumption that does not hold in many real-world applications, such as e.g. medicine. To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data. In our approach, we create a representation of the missing pattern, which is incorporated into mixed mode mixture models in such a way that the information provided by the missing patterns is effectively exploited. Moreover, we also propose a semi-supervised kernel, capable of taking advantage of incomplete label information to learn more accurate similarities. Experiments on benchmark data, as well as a real-world case study of patients described by longitudinal electronic health record data who potentially suffer from hospital-acquired infections, demonstrate the effectiveness of the proposed methods.