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 1120 of 113 papers

TitleStatusHype
Algorithms for Learning Graphs in Financial MarketsCode0
Deep Markov Spatio-Temporal FactorizationCode0
A time resolved clustering method revealing longterm structures and their short-term internal dynamicsCode0
Learning Representations for Incomplete Time Series ClusteringCode0
Discovering patterns of online popularity from time seriesCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
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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