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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 113 papers

TitleStatusHype
On time series clustering with k-means—0
PARIS: Personalized Activity Recommendation for Improving Sleep Quality—0
Polyspectral Mean based Time Series Clustering of Indian Stock Market—0
Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Reducing statistical time-series problems to binary classification—0
Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering—0
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences—0
Similarity Preserving Representation Learning for Time Series Clustering—0
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2—0
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
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—0
Towards Financially Inclusive Credit Products Through Financial Time Series Clustering—0
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals—0
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Unsupervised Visual Time-Series Representation Learning and 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