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 1–25 of 113 papers

TitleStatusHype
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine MonitoringCode1
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
k-Graph: A Graph Embedding for Interpretable Time Series ClusteringCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients—0
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers—0
Deep Temporal Contrastive Clustering—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
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
Concrete Dense Network for Long-Sequence Time Series Clustering—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
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—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
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