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 51–100 of 113 papers

TitleStatusHype
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
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
Deep Temporal Contrastive Clustering—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—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
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
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
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Hydroclimatic time series features at multiple time scales—0
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data—0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering—0
Interpretable Time Series Clustering Using Local Explanations—0
K-ARMA Models for Clustering Time Series Data—0
Kernel Spectral Clustering and applications—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale—0
CRAD: Clustering with Robust Autocuts and DepthCode0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Deep Markov Spatio-Temporal FactorizationCode0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
Time Series Clustering via Community Detection in NetworksCode0
Learning Representations for Incomplete Time Series ClusteringCode0
Discovering patterns of online popularity from time seriesCode0
Learning Representations for Time Series ClusteringCode0
Linear Dynamics: Clustering without identificationCode0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
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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