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 5160 of 113 papers

TitleStatusHype
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNsCode0
Time Series Clustering for Grouping Products Based on Price and Sales Patterns0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity0
Hydroclimatic time series features at multiple time scales0
Unsupervised Visual Time-Series Representation Learning and Clustering0
Coresets for Time Series Clustering0
PARIS: Personalized Activity Recommendation for Improving Sleep Quality0
Time Series Clustering for Human Behavior Pattern Mining0
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations0
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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