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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 113 papers

TitleStatusHype
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
k-Graph: A Graph Embedding for Interpretable Time Series ClusteringCode1
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine MonitoringCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter AveragingCode0
Algorithms for Learning Graphs in Financial MarketsCode0
Linear Dynamics: Clustering without identificationCode0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
Learning Representations for Time Series ClusteringCode0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
A time resolved clustering method revealing longterm structures and their short-term internal dynamicsCode0
Discovering patterns of online popularity from time seriesCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Deep Markov Spatio-Temporal FactorizationCode0
Learning Representations for Incomplete Time Series ClusteringCode0
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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