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

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Showing 31–40 of 113 papers

TitleStatusHype
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
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
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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