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Time Series Clustering
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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
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Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
A system identification approach to clustering vector autoregressive time series
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering
4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients
Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering
Polyspectral Mean based Time Series Clustering of Indian Stock Market
Examining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers
Bridging the Gap: A Decade Review of Time-Series Clustering Methods
TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors
Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
On time series clustering with k-means
Spacecraft inertial parameters estimation using time series clustering and reinforcement learning
Time Series Clustering with General State Space Models via Stochastic Variational Inference
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
Concrete Dense Network for Long-Sequence Time Series Clustering
Towards Financially Inclusive Credit Products Through Financial Time Series Clustering
Evaluation of k-means time series clustering based on z-normalization and NP-Free
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology
Time Series Clustering With Random Convolutional Kernels
Time series clustering based on prediction accuracy of global forecasting models
Fuzzy clustering of ordinal time series based on two novel distances with economic applications
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