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 1–10 of 113 papers

TitleStatusHype
Volatility Spillovers and Interconnectedness in OPEC Oil Markets: A Network-Based log-ARCH Approach—0
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals—0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
A system identification approach to clustering vector autoregressive time series—0
4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients—0
Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering—0
Polyspectral Mean based Time Series Clustering of Indian Stock Market—0
Examining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation—0
k-Graph: A Graph Embedding for Interpretable Time Series ClusteringCode1
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers—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