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 71–80 of 113 papers

TitleStatusHype
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
A Benchmark Study on Time Series Clustering—0
Deep Markov Spatio-Temporal FactorizationCode0
Autoencoder-based time series clustering with energy applications—0
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
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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