SOTAVerified

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 81–90 of 113 papers

TitleStatusHype
Actor-Critic Approach for Temporal Predictive Clustering—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
Approximate Collapsed Gibbs Clustering with Expectation Propagation—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
A self-organising eigenspace map for time series clustering—0
A system identification approach to clustering vector autoregressive time series—0
Autoencoder-based time series clustering with energy applications—0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series 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