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Time Series Clustering

Time Series Clustering is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.

Source: Comprehensive Process Drift Detection with Visual Analytics

Papers

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Showing 11–20 of 113 papers

TitleStatusHype
A self-organising eigenspace map for time series clustering—0
A system identification approach to clustering vector autoregressive time series—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Autoencoder-based time series clustering with energy applications—0
Approximate Collapsed Gibbs Clustering with Expectation Propagation—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Clustering evolving data using kernel-based methods—0
Bridging the Gap: A Decade Review of Time-Series Clustering Methods—0
Clustering Macroeconomic Time Series—0
Actor-Critic Approach for Temporal Predictive Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SOM-VAE-probNMI (physiology_6_hours)0.05—Unverified
2k-meansNMI (physiology_6_hours)0.04—Unverified
3SOM-VAENMI (physiology_6_hours)0.04—Unverified