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 26–50 of 113 papers

TitleStatusHype
Concrete Dense Network for Long-Sequence Time Series Clustering—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Coresets for Time Series Clustering—0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
Kernel Spectral Clustering and applications—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Deep Temporal Contrastive Clustering—0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
A Benchmark Study on Time Series Clustering—0
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest—0
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping—0
Clustering evolving data using kernel-based methods—0
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data—0
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering—0
Dynamic clustering of time series data—0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
Evaluation of k-means time series clustering based on z-normalization and NP-Free—0
Examining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data—0
Fuzzy clustering of ordinal time series based on two novel distances with economic applications—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Hydroclimatic time series features at multiple time scales—0
Show:102550
← PrevPage 2 of 5Next →

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