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 76–100 of 113 papers

TitleStatusHype
Dynamic clustering of time series data—0
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification—0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
A time resolved clustering method revealing longterm structures and their short-term internal dynamicsCode0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Learning Representations for Time Series ClusteringCode0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Actor-Critic Approach for Temporal Predictive Clustering—0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
Linear Dynamics: Clustering without identificationCode0
Clustering piecewise stationary processes—0
A self-organising eigenspace map for time series clustering—0
Discovering patterns of online popularity from time seriesCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Time series clustering based on the characterisation of segment typologies—0
Approximate Collapsed Gibbs Clustering with Expectation Propagation—0
Clustering Macroeconomic Time Series—0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Using Quantum Mechanics to Cluster Time Series—0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game 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