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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 26–50 of 113 papers

TitleStatusHype
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest—0
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter AveragingCode0
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping—0
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study—0
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data—0
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
Time Series Clustering With Random Convolutional KernelsCode0
Time series clustering based on prediction accuracy of global forecasting models—0
Fuzzy clustering of ordinal time series based on two novel distances with economic applications—0
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time SeriesCode0
Deep Temporal Contrastive Clustering—0
Uncertainty-DTW for Time Series and SequencesCode0
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences—0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
Interpretable Time Series Clustering Using Local Explanations—0
K-ARMA Models for Clustering Time Series Data—0
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine MonitoringCode1
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—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