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 76–100 of 113 papers

TitleStatusHype
Using Quantum Mechanics to Cluster Time Series—0
4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients—0
Volatility Spillovers and Interconnectedness in OPEC Oil Markets: A Network-Based log-ARCH Approach—0
A Benchmark Study on Time Series Clustering—0
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers—0
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
Bridging the Gap: A Decade Review of Time-Series Clustering Methods—0
Clustering evolving data using kernel-based methods—0
Clustering Macroeconomic Time Series—0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study—0
Clustering piecewise stationary processes—0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework—0
Concrete Dense Network for Long-Sequence Time Series Clustering—0
Coresets 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