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

Showing 110 of 113 papers

TitleStatusHype
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine MonitoringCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
k-Graph: A Graph Embedding for Interpretable Time Series ClusteringCode1
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
Algorithms for Learning Graphs in Financial MarketsCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SOM-VAE-probNMI (physiology_6_hours)0.05Unverified
2k-meansNMI (physiology_6_hours)0.04Unverified
3SOM-VAENMI (physiology_6_hours)0.04Unverified