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 41–50 of 113 papers

TitleStatusHype
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
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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