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 51–75 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 Patterns—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Hydroclimatic time series features at multiple time scales—0
Unsupervised Visual Time-Series Representation Learning and Clustering—0
Coresets for Time Series Clustering—0
PARIS: Personalized Activity Recommendation for Improving Sleep Quality—0
Time Series Clustering for Human Behavior Pattern Mining—0
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations—0
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale—0
Learning Representations for Incomplete Time Series ClusteringCode0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Algorithms for Learning Graphs in Financial MarketsCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
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
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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