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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 1–50 of 113 papers

TitleStatusHype
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
k-Graph: A Graph Embedding for Interpretable Time Series ClusteringCode1
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine MonitoringCode1
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
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
A system identification approach to clustering vector autoregressive time series—0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
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
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers—0
Bridging the Gap: A Decade Review of Time-Series Clustering Methods—0
TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering—0
Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors—0
Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering—0
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework—0
On time series clustering with k-means—0
Spacecraft inertial parameters estimation using time series clustering and reinforcement learning—0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
Concrete Dense Network for Long-Sequence Time Series Clustering—0
Towards Financially Inclusive Credit Products Through Financial Time Series Clustering—0
Evaluation of k-means time series clustering based on z-normalization and NP-Free—0
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data—0
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering—0
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering—0
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest—0
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter AveragingCode0
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping—0
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study—0
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data—0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology—0
Time Series Clustering With Random Convolutional KernelsCode0
Time series clustering based on prediction accuracy of global forecasting models—0
Fuzzy clustering of ordinal time series based on two novel distances with economic applications—0
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time SeriesCode0
Deep Temporal Contrastive Clustering—0
Uncertainty-DTW for Time Series and SequencesCode0
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences—0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
Interpretable Time Series Clustering Using Local Explanations—0
K-ARMA Models for Clustering Time Series Data—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