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 91–100 of 113 papers

TitleStatusHype
Learning Representations for Incomplete Time Series ClusteringCode0
Discovering patterns of online popularity from time seriesCode0
Learning Representations for Time Series ClusteringCode0
Linear Dynamics: Clustering without identificationCode0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
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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