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

2017/04/03 by Karl Øyvind Mikalsen, Mikalsen, Karl Øyvind, Filippo Maria Bianchi +6 · 3 citations
Computer Science · Engineering · Mathematics · #Advanced Chemical Sensor Technologies #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Time Series Analysis and Forecasting #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1704.00794

23 pages, 6 figures

openalex publication_date 2017/04/03 · arxiv created 2017/06/29 · arxiv updated 2017/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Autoregressive Kernels For Time Series
• Inference and missing data
• Review: A gentle introduction to imputation of missing values

Cited by

• InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis
• Learning compressed representations of blood samples time series with missing data
• Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations

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