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 111113 of 113 papers

TitleStatusHype
An Empirical Evaluation of Similarity Measures for Time Series Classification0
Model-based clustering with Hidden Markov Model regression for time series with regime changes0
Reducing statistical time-series problems to binary classification0
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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