# Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

arXiv: https://arxiv.org/abs/1704.00794 · HTML: https://mlsanity.com/paper/1704.00794/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 11 of 11)

- [A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2305.18888/) (2023)
- [Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations](https://mlsanity.com/paper/2402.12598/) (2024)
- [The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making](https://mlsanity.com/paper/2301.08970/) (2023)
- [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)
- [Clustering individuals based on multivariate EMA time-series data](https://mlsanity.com/paper/2212.01159/) (2022)
- [Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series](https://mlsanity.com/paper/2312.01294/) (2023)
- [Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization](https://mlsanity.com/paper/2606.12077/) (2026)
- [Approaching adverse event detection utilizing transformers on clinical time-series](https://mlsanity.com/paper/2311.09165/) (2023)
- [Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning](https://mlsanity.com/paper/2312.05698/) (2023)
- [Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations](https://mlsanity.com/paper/2504.17717/) (2025)
- [InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis](https://mlsanity.com/paper/2506.08884/) (2025)

## Abstract (arXiv)

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust time series cluster kernel (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.