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 1–50 of 113 papers

TitleStatusHype
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine MonitoringCode1
Graph-based Time Series Clustering for End-to-End Hierarchical ForecastingCode1
k-Graph: A Graph Embedding for Interpretable Time Series ClusteringCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNsCode0
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
Algorithms for Learning Graphs in Financial MarketsCode0
Time Series Clustering via Community Detection in NetworksCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Discovering patterns of online popularity from time seriesCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
Time Series Clustering With Random Convolutional KernelsCode0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
Linear Dynamics: Clustering without identificationCode0
Uncertainty-DTW for Time Series and SequencesCode0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image SequencesCode0
A time resolved clustering method revealing longterm structures and their short-term internal dynamicsCode0
Deep Markov Spatio-Temporal FactorizationCode0
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series ClusteringCode0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
Learning Representations for Incomplete Time Series ClusteringCode0
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter AveragingCode0
Learning Representations for Time Series ClusteringCode0
Autoencoder-based time series clustering with energy applications—0
Coresets for Time Series Clustering—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Concrete Dense Network for Long-Sequence Time Series Clustering—0
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework—0
A system identification approach to clustering vector autoregressive time series—0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Clustering piecewise stationary processes—0
A self-organising eigenspace map for time series clustering—0
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers—0
Fuzzy clustering of ordinal time series based on two novel distances with economic applications—0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind 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