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 61–70 of 113 papers

TitleStatusHype
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations—0
Spacecraft inertial parameters estimation using time series clustering and reinforcement learning—0
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets—0
Time series clustering based on prediction accuracy of global forecasting models—0
Time series clustering based on the characterisation of segment typologies—0
Time Series Clustering for Grouping Products Based on Price and Sales Patterns—0
Time Series Clustering for Human Behavior Pattern Mining—0
Time Series Structure Discovery via Probabilistic Program Synthesis—0
Times series averaging from a probabilistic interpretation of time-elastic kernel—0
TNNGen: Automated Design of Neuromorphic Sensory Processing Units 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