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 21–30 of 113 papers

TitleStatusHype
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Linear Dynamics: Clustering without identificationCode0
A time resolved clustering method revealing longterm structures and their short-term internal dynamicsCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
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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