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

Showing 101110 of 113 papers

TitleStatusHype
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Time Series Clustering With Random Convolutional KernelsCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Algorithms for Learning Graphs in Financial MarketsCode0
A time resolved clustering method revealing longterm structures and their short-term internal dynamicsCode0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
Uncertainty-DTW for Time Series and SequencesCode0
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNsCode0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SOM-VAE-probNMI (physiology_6_hours)0.05Unverified
2k-meansNMI (physiology_6_hours)0.04Unverified
3SOM-VAENMI (physiology_6_hours)0.04Unverified