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
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
Deep Markov Spatio-Temporal FactorizationCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
Autoencoder-based time series clustering with energy applications—0
Coresets for Time Series Clustering—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Concrete Dense Network for Long-Sequence Time Series Clustering—0
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework—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